{"datasets":[{"slug":"para-handcycling-sci-healer-multimodal","name":"A Longitudinal and Multimodal physiological signals dataset of Para handcycling training and race for SCI Rehabilitation","summary":"The data dictionary (metadata.pdf Table 4) lists these channels: - ECG leads I/II at 500 Hz, chest; derived HR, HRV, PQR. - Precordial v1–v6 at 500 Hz (R3 only). - Respiration at 50 Hz from thoracic, xiphoid, and abdominal straps; derived RR.","signals":["ecg","resp","temp","spo2","accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":null,"doi":"10.5281/zenodo.23168228","url":"/research/datasets/para-handcycling-sci-healer-multimodal"},{"slug":"multimodal-nback-music","name":"A Multimodal Dataset for Investigating Working Memory in Presence of Music","summary":null,"signals":["ppg","ecg","eda","eeg","resp","emg","skin-temp","accel"],"ppg_sites":["finger","wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"ODC-By 1.0","gated":null,"wrist":true,"source_url":"https://physionet.org/content/multimodal-nback-music/1.0.0/","doi":"10.13026/6vh4-dk68","url":"/research/datasets/multimodal-nback-music"},{"slug":"aauwss","name":"AAUWSS","summary":"AAUWSS (Djanian / Nielsen / Nielsen / Bruun, Aalborg University + Aarhus University Hospital Respiration Center West). Collected Mar 24–Apr 4 2023. Release includes raw Empatica CSVs, PSG EDFs, AASM annotations, and alignedsleepdataset pickles (30 s epochs: ECG 30×200 Hz, PPG 30×64 Hz + labels).","signals":["ppg","ecg","accel","skin-temp","eda","eeg","emg","eog","psg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":13,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/16919071","doi":"10.5281/zenodo.16919071","url":"/research/datasets/aauwss"},{"slug":"adarp-aud-stress","name":"ADARP (AUD EMA stress, E4)","summary":"Alcohol and Drug Abuse Research Program pilot: continuous E4 physiology + EMA stress/craving in alcohol-use-disorder recovery. Zenodo DOI 10.5281/zenodo.6640290. Related arXiv 2206.14568.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":11,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/6640290","doi":"10.5281/zenodo.6640290","url":"/research/datasets/adarp-aud-stress"},{"slug":"aspen-adelaide-wearable-psg","name":"Adelaide ASPEN wearable-PSG garment","summary":null,"signals":["ecg","resp","skin-temp"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":7,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://doi.org/10.6084/m9.figshare.33399838.v1","doi":"10.6084/m9.figshare.33399838.v1","url":"/research/datasets/aspen-adelaide-wearable-psg"},{"slug":"afpdb","name":"AFPDB","summary":"PhysioNet afpdb 1.0.0 (Moody / Goldberger / McClennen / Swiryn). Learning records n (no documented AF) and p (PAF subjects, with 5-min continuation c showing PAF onset on even-numbered precede records). Test records t01–t100 without continuation files.","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":98,"license":"ODC-By 1.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/afpdb/1.0.0/","doi":null,"url":"/research/datasets/afpdb"},{"slug":"ai-readi","name":"AI-READI","summary":"Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights. Versioned releases on FAIRhub: v1.0.0 (2024-05-03, DOI 10.60775/fairhub.1), v2.0.0 (2024-11-08, DOI 10.60775/fairhub.2; 1,067 participants, data collected 2023-07-19 → 2024-07-31), v3.0.0 (2025-11-17, DOI 10.60775/fairhub.3; 2,280 participants per the v3 healthsheet). Annual releases planned.","signals":["hr","spo2","accel","cgm","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":2280,"license":"AI-READI custom Health Data License (v2.0, Zenodo 10.5281/zenodo.17555036); T2D-related research only","gated":true,"wrist":true,"source_url":"https://fairhub.io/datasets/3","doi":"10.60775/fairhub.3","url":"/research/datasets/ai-readi"},{"slug":"airwave-health-ppg-pwv","name":"Airwave Health Monitoring Study","summary":"Occupational health / environmental epidemiology cohort of UK police officers exposed to the Airwave (TETRA) radio system, run at Imperial College London with a linked tissue bank. Design paper: Elliott et al., Environmental Research (2014), DOI 10.1016/j.envres.2014.07.025.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"Gated access by application (DPUK / UK LLC / Imperial TRE)","gated":true,"wrist":false,"source_url":"https://police-health.org.uk/","doi":"10.1016/j.envres.2014.07.025","url":"/research/datasets/airwave-health-ppg-pwv"},{"slug":"alameda-pd","name":"alameda-pd","summary":null,"signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":11,"license":null,"gated":null,"wrist":null,"source_url":null,"doi":"10.5281/zenodo.15769959","url":"/research/datasets/alameda-pd"},{"slug":"aquaset","name":"AQUASET","summary":"AQUASET (Affective Physiological QUAlity-of-Experience Streaming service dataSET) combines streaming QoE parameters, subjective assessments, physiological signals, and facial-expression features. Zenodo 22741195, DOI 10.5281/zenodo.22741195, license CC BY 4.0.","signals":["ppg","ecg","accel","skin-temp","eda","eeg","ibi"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":30,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/22741195","doi":"10.5281/zenodo.22741195","url":"/research/datasets/aquaset"},{"slug":"arwise","name":"ArWISE Vol4","summary":"ArWISE (CASAS / Diane Cook) is a multi-volume smartwatch free-living activity dataset. Sensor streams are 10 Hz accelerometer, gyroscope, and relative location (distance from home); portions carry user activity labels. Volume 4 DOI 10.5281/zenodo.15809156.","signals":["accel","gyro","hr"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":46,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/15809156","doi":"10.5281/zenodo.15809156","url":"/research/datasets/arwise"},{"slug":"assemblyhar-wrist-acc-manual-assembly","name":"AssemblyHAR","summary":"Wrist-worn triaxial accelerometer recordings from 16 participants over 26 sessions of two manual assembly processes. RawData: original ACC recordings, button-based annotation logs, and BLE connection logs. ProcessedData: labelled and preprocessed recordings, validation reports, summary statistics, XES event logs for process mining, and windowed HAR datasets with grouped and ungrouped activity labels in predefined train/val/test partitions.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":null,"doi":"10.35097/46ntdksar70pfmmn","url":"/research/datasets/assemblyhar-wrist-acc-manual-assembly"},{"slug":"auricle","name":"Auricle","summary":"Benchmark built to compare wearable cardiovascular monitors on one common, high-rate dataset. All sensors run on one BIOPAC MP160 at 10 kHz, then downsampled to 1 kHz. Stressors change hemodynamics: breath-hold ×4, light exercise ×2, cold pressor ×1 (healthy only), Valsalva ×4, in a Latin-square order.","signals":["ppg","ecg","accel","audio","abp","resp"],"ppg_sites":["finger","ear"],"ppg_wavelengths_nm":null,"bp_reference":["finapres","cuff"],"subjects":70,"license":"ODC-By 1.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/auricle/1.0.0/","doi":"10.13026/swme-vp69","url":"/research/datasets/auricle"},{"slug":"bandx-activity","name":"bandx-activity","summary":null,"signals":["accel","gyro"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":33,"license":null,"gated":null,"wrist":null,"source_url":null,"doi":"10.5281/zenodo.14543551","url":"/research/datasets/bandx-activity"},{"slug":"barocontrol-healthy","name":"Barocontrol in healthy subjects (Mol/van Wezel, Radboud)","summary":"Collection Barocontrol in healthy subjects (version 1, published 2020-02-24), Donders Institute / Radboud University, di.dcn.DSC6200245101149. Authors: Arjen Mol, Andrea Maier, Richard J.A. van Wezel, Carel Meskers.","signals":["ppg","ecg","abp"],"ppg_sites":["finger","wrist"],"ppg_wavelengths_nm":null,"bp_reference":["finapres"],"subjects":34,"license":"RU-DI-HD-1.0: Radboud Data Repository data use agreement for identifiable human data","gated":true,"wrist":"mixed","source_url":"https://data.ru.nl/collections/di/dcn/DSC_62002451_01_149","doi":"10.34973/7myp-f567","url":"/research/datasets/barocontrol-healthy"},{"slug":"bed-bcg-carlson","name":"Bed-Based BCG (Carlson, K-State)","summary":"Kansas State University (Carlson, Turpin, Suliman, Ade, Warren, Thompson). Paper: Sensors 2021, 21, 156, https://doi.org/10.3390/s21010156.","signals":["bcg","ecg","ppg","resp","abp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["finapres"],"subjects":40,"license":"CC BY 4.0 + IEEE DataPort Terms of Use","gated":null,"wrist":false,"source_url":"https://ieee-dataport.org/open-access/bed-based-ballistocardiography-dataset","doi":"10.21227/77hc-py84","url":"/research/datasets/bed-bcg-carlson"},{"slug":"biagetti","name":"biagetti","summary":null,"signals":["ppg","accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":7,"license":null,"gated":null,"wrist":null,"source_url":null,"doi":"10.1016/j.dib.2019.105044","url":"/research/datasets/biagetti"},{"slug":"bidmc","name":"BIDMC PPG and Respiration (Pimentel 2016 / PhysioNet)","summary":"Not re-downloaded.","signals":["ppg","ecg","resp","spo2"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":53,"license":"ODC-By 1.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/bidmc/1.0.0/","doi":null,"url":"/research/datasets/bidmc"},{"slug":"bidsleep","name":"BIDSleep","summary":"BIDSleep (PhysioNet bidsleep-dataset) pairs consumer smartwatch instantaneous heart rate (IHR) and wrist accelerometry with expert-reviewed Dreem 2 EEG hypnograms. Authors: Song / Zhang / Zhou / Dutta (UMass Lowell). Companion paper: IEEE TBME 2025 (10.1109/TBME.2025.3612158).","signals":["accel","hr","eeg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":47,"license":"ODC-By 1.0","gated":null,"wrist":true,"source_url":"https://physionet.org/content/bidsleep-dataset/1.0.1/","doi":"10.13026/rees-1092","url":"/research/datasets/bidsleep"},{"slug":"big-ideas-glycemic","name":"BIG IDEAs Lab Glycemic Variability and Wearable Device Data","summary":"PhysioNet big-ideas-glycemic-wearable v1.1.3 (DOI 10.13026/aw6y-fc44, published April 13, 2026). Continuous free-living wrist PPG with interstitial glucose.","signals":["ppg","accel","skin-temp","eda","cgm"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":16,"license":"ODC-By 1.0","gated":null,"wrist":true,"source_url":"https://physionet.org/content/big-ideas-glycemic-wearable/1.1.3/","doi":"10.13026/aw6y-fc44","url":"/research/datasets/big-ideas-glycemic"},{"slug":"biostress","name":"BIOSTRESS (psychologist induction + exam)","summary":"Psychological-stress dataset pairing a psychologist-led induction session with later exam recordings (Data-in-Brief 2023). Mendeley DOI 10.17632/z9y2fn38fp.1. Therapy folders prefixed T, exam folders prefixed E.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":20,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://data.mendeley.com/datasets/z9y2fn38fp/1","doi":"10.17632/z9y2fn38fp.1","url":"/research/datasets/biostress"},{"slug":"blue-green-ppg-shchelkanova","name":"Blue and Green PPG (Shchelkanova / Mendeley)","summary":"Shchelkanova et al., Kyushu Institute of Technology (Jun 2021). Includes derived heartbeat intervals + LabVIEW software.","signals":["ppg","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":12,"license":null,"gated":null,"wrist":false,"source_url":"https://data.mendeley.com/datasets/6m8kmdv8ys/1","doi":"10.17632/6m8kmdv8ys.1","url":"/research/datasets/blue-green-ppg-shchelkanova"},{"slug":"blumio-cv-sensors","name":"Blumio","summary":"Blumio, Inc. (Gomes, Liao, Shay, Bikhchandani). Western IRB-approved protocol; released 2021 as Open Access.","signals":["ppg","abp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["cnap"],"subjects":115,"license":"CC BY 4.0 + IEEE DataPort Terms of Use","gated":null,"wrist":"mixed","source_url":"https://ieee-dataport.org/open-access/dataset-synchronized-signals-wearable-cardiovascular-monitoring-sensors","doi":"10.21227/3yte-wz05","url":"/research/datasets/blumio-cv-sensors"},{"slug":"bmd-hs","name":"BMD-HS","summary":"864 heart-sound recordings across AS, AR, MR, MS, multi-disease and normal, multi-label with echocardiogram-verified diagnoses (arXiv 2409.00724; CMPB Update 2026). Source: GitHub README + arXiv abstract (via search, 2026-10-12).","signals":["audio"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY-NC-ND 4.0","gated":null,"wrist":false,"source_url":"https://github.com/sani002/BMD-HS-Dataset","doi":"10.1016/j.cmpbup.2026.100237","url":"/research/datasets/bmd-hs"},{"slug":"brist1d","name":"BrisT1D-Open","summary":"BrisT1D was conducted by Sam Gordon James, Miranda Armstrong, Aisling O'Kane, Harry Emerson, and Zahraa Abdallah at the University of Bristol. Participants used smartwatches alongside their T1D self-management and donated smartwatch/T1D-device data during the six-month study. The BrisT1D-Open record contains the processed state; the raw state is a separate restricted-access dataset.","signals":["hr","cgm","steps","distance","calories","activity-derived"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":24,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://data.bris.ac.uk/data/dataset/33z5jc8fa6tob21ptrugzqog08","doi":"10.5523/bris.33z5jc8fa6tob21ptrugzqog08","url":"/research/datasets/brist1d"},{"slug":"butppg","name":"Brno University of Technology Smartphone PPG Database (BUT PPG)","summary":null,"signals":["ppg","ecg"],"ppg_sites":["face-camera","finger"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":50,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/butppg/2.0.0/","doi":"10.13026/tn53-8153","url":"/research/datasets/butppg"},{"slug":"cachet-cadb","name":"CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset","summary":"24 patients with known or suspected paroxysmal AF: 7 Danish and 17 Indian, 15 M / 9 F, mean age 59. They wore a Movisens EcgMove4 chest Holter (mCardia system, REAFEL study) in free living for 24 h to 3 weeks, giving 259 days in total. Raw signals: ECG at 1024 Hz, ACC and gyroscope at 64 Hz, pressure at 8 Hz.","signals":["ecg","accel","gyro","barometer"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":null,"doi":"10.11583/DTU.14547264","url":"/research/datasets/cachet-cadb"},{"slug":"can-stress","name":"CAN-STRESS (ambulatory E4 stress 0–9)","summary":"Real-world ambulatory multimodal stress + cannabis-use dataset. Zenodo DOI 10.5281/zenodo.14842061. ~24 h wear during daily activities.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":82,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/14842061","doi":"10.5281/zenodo.14842061","url":"/research/datasets/can-stress"},{"slug":"capture-24","name":"capture-24","summary":"CAPTURE-24 (OxWearables / Oxford): free-living wrist accelerometry with wearable-camera activity annotations. Nature Scientific Data descriptor; ORA release.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":151,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://ora.ox.ac.uk/objects/uuid:99d7c092-d865-4a19-b096-cc16440cd001","doi":"10.5287/bodleian:NGx0JOMP5","url":"/research/datasets/capture-24"},{"slug":"cardiowave-portable","name":"CardioWave-Portable","summary":"CardioWave-Portable (Yang / Zhang / Liu / Wang / Xie / Guo; Harbin Institute of Technology affiliation on Zenodo). Zenodo record 20912312 (concept 20799109), published 2026-06-26. Release zip includes waveforms, data dictionary, loading code, and P1/P2/P3 benchmark scripts.","signals":["ppg","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":["cuff"],"subjects":979,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/20912312","doi":"10.5281/zenodo.20912312","url":"/research/datasets/cardiowave-portable"},{"slug":"chapman-shaoxing-ningbo","name":"Chapman–Shaoxing–Ningbo 12-lead ECG arrhythmia database (PhysioNet)","summary":"10-s resting 12-lead ECGs from 45,152 patients (Chapman University, Shaoxing People's Hospital, Ningbo First Hospital), GE MUSE; rhythm labels (incl. AFIB) and conditions (PVC, RBBB, LBBB, APB) by two physicians, SNOMED-CT coded. Earlier 10,646-patient subset on figshare (10.6084/m9.figshare.c.4560497.v2).","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":45152,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/ecg-arrhythmia/1.0.0/","doi":"10.13026/wgex-er52","url":"/research/datasets/chapman-shaoxing-ningbo"},{"slug":"challenge-2015","name":"CinC / PhysioNet Challenge 2015","summary":"Official 2015 PhysioNet/CinC Challenge corpus (Clifford et al.). Training set: 750 recordings with expert true/false alarm labels; hidden test 500 remains private. Each record leads up to an alarm at t≈5 min; retrospective subset adds 30 s post-alarm.","signals":["ppg","ecg","abp"],"ppg_sites":["ear","finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":null,"license":"ODC-By 1.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/challenge-2015/1.0.0/","doi":null,"url":"/research/datasets/challenge-2015"},{"slug":"clas","name":"CLAS","summary":"CLAS (Cognitive Load, Affect and Stress) supports automated recognition of emotion, stress, and cognitive load. Authors: Markova / Ganchev / Kalinkov (TU Varna). Paper: BIA-2019 10.1109/BIA48344.2019.8967457.","signals":["ppg","ecg","accel","eda"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":62,"license":"Academic use EULA, free of charge on request from the authors","gated":null,"wrist":"mixed","source_url":"https://ieee-dataport.org/open-access/database-cognitive-load-affect-and-stress-recognition","doi":"10.21227/ybsw-yr53","url":"/research/datasets/clas"},{"slug":"clbp-400","name":"CLBP-400","summary":"400 HD face videos at 60 fps (iPhone 16 Pro Max, Nikon D5300, Elgato 4K60), 30-60 s each, ~83 GB, indoor and outdoor with lux values; ground truth SBP/DBP/HR (BP device not stated). Preprint preprints.org 202604.1079. Access by DUA, non-commercial academic only → gated / eval-only.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":400,"license":"Data use agreement: non-commercial academic research only; no redistribution of raw video","gated":null,"wrist":null,"source_url":"https://github.com/alialnaji/CLBP-300-Dataset","doi":null,"url":"/research/datasets/clbp-400"},{"slug":"cleveland-family-study","name":"Cleveland Family Study (CFS)","summary":"Redline et al. 1990–2006, up to four visits over 16 years. Distributed by NSRR.","signals":["psg","ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":2284,"license":"NSRR DUA; \"only available for non-commercial use\" (sleepdata.org)","gated":true,"wrist":false,"source_url":"https://sleepdata.org/datasets/cfs","doi":"10.25822/jmyx-mz90","url":"/research/datasets/cleveland-family-study"},{"slug":"feng-imu-layouts-2026","name":"Comprehensive IMU dataset for sensor layouts in HAR + intensity (Feng)","summary":"Figshare v2 (2025-09-25, Mingfei Feng). Page text gives title only. Awesome list: 30 subjects.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":null,"gated":null,"wrist":null,"source_url":"https://figshare.com/articles/dataset/Comprehensive-IMU-Dataset/30193942","doi":null,"url":"/research/datasets/feng-imu-layouts-2026"},{"slug":"bp-graphene-bioimpedance","name":"Continuous cuffless BP via graphene bioimpedance tattoos (PhysioNet)","summary":"PhysioNet bp-graphene-bioimpedance v1.0.0.","signals":["ppg","abp","bioimpedance"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["finapres"],"subjects":8,"license":"ODC-By 1.0","gated":null,"wrist":"mixed","source_url":"https://physionet.org/content/bp-graphene-bioimpedance/1.0.0/","doi":null,"url":"/research/datasets/bp-graphene-bioimpedance"},{"slug":"covid-lockdown-gear-sport","name":"COVID Lockdown Gear Sport (longitudinal wearable + EMA)","summary":"Intensive longitudinal physiological + emotional assessment of college students before/during/after the 2020 COVID-19 lockdown (Southern California). Dryad DOI 10.7280/D1WH6T. ~8.6 GB.","signals":["ppg","accel"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC0 1.0","gated":null,"wrist":true,"source_url":"https://datadryad.org/dataset/doi:10.7280/D1WH6T","doi":null,"url":"/research/datasets/covid-lockdown-gear-sport"},{"slug":"crhe-long-term-ppg-bp","name":"CRHE","summary":"Fudan University (Yuhui Cen, Jingjing Luo) with Univ. of Shanghai for Science and Technology (Hongliu Yu) and Changzhou Anxin Yihe and Rehabilitation Hospital (Yufeng Bao, Bingzhong Huang). Abstract: \"self-collected multi-wavelength, dual-24h PPG-BP dataset … circadian rhythm annotations … collected using wearable devices and ambulatory BP monitors.\" Paper: tpf-bp-physiology-guided-ssl-personalized-cuffless-bp-crhe-2026.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":["abpm"],"subjects":29,"license":"MIT (GitHub release); CC BY 4.0 (IEEE DataPort release)","gated":null,"wrist":false,"source_url":"https://github.com/FDU-Bio-Monitoring/CRHE-Long-Term-PPG-BP-Dataset","doi":"10.21227/bgnp-xs19","url":"/research/datasets/crhe-long-term-ppg-bp"},{"slug":"d1namo","name":"D1NAMO","summary":"D1NAMO was collected by the D1NAMO project with authors from the University of Applied Sciences and Arts Western Switzerland, EPFL, Open University of the Netherlands, and Hôpital Riviera-Chablais. The Zenodo record publishes separate healthy and diabetes subsets for ECG, sensor data, and food/glucose annotations. The companion article is documented at 10.1016/j.imu.2018.09.003.","signals":["ecg","accel","resp","cgm"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":29,"license":"CC BY-SA 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/5651217","doi":"10.5281/zenodo.5651217","url":"/research/datasets/d1namo"},{"slug":"daily-and-sports-activities","name":"daily-and-sports-activities","summary":null,"signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":8,"license":null,"gated":null,"wrist":null,"source_url":null,"doi":"10.24432/C5C59F","url":"/research/datasets/daily-and-sports-activities"},{"slug":"daphnet-fog","name":"Daphnet Freezing of Gait","summary":"Three acceleration sensors at hip and leg of Parkinson's disease patients with freezing of gait; recorded in the lab (Tel Aviv Sourasky Medical Center, 2008): straight walking, turns, and an ADL task. UCI, CC BY 4.0. N=10 and 8.3 h per the original dataset documentation listed on Awesome-IMU-Sensing (not re-checked).","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":10,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://archive.ics.uci.edu/dataset/245/daphnet+freezing+of+gait","doi":null,"url":"/research/datasets/daphnet-fog"},{"slug":"deo","name":"DEO","summary":"Defined in Riveros et al., “Diffusion-Based Synthetic Data Pretraining for Enhancing Activity Recognition” (arXiv 2610.02292, 2026-10-01). Authors start from a Daily Living Activities Dataset (90 participants, 13 activities, Samsung Galaxy Watch 4 wrist IMU) and relabel into DEO: drinking + eating from the dominant hand; other = all non-alimentary activities from the opposite hand.","signals":["accel","gyro","magnetometer"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":90,"license":null,"gated":null,"wrist":true,"source_url":"https://arxiv.org/abs/2610.02292","doi":null,"url":"/research/datasets/deo"},{"slug":"dose-i","name":"DOSE-I","summary":"DOSE-I (Garbe / Kantelhardt / Seeliger / Schmid; Universitätsmedizin Halle). Prospective single-center endoscopy sedation study (DRKS00016605; May 2019–Jul 2021). Export via MIB/RS232 + ixTrend Express.","signals":["ppg","ecg","eeg","resp","spo2","hr"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":171,"license":"CC BY 4.0 + data use agreement (no re-identification)","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/22145442","doi":"10.5281/zenodo.22145442","url":"/research/datasets/dose-i"},{"slug":"dreamt","name":"DREAMT","summary":"Wrist PPG with PSG gold-standard sleep staging — the largest single wrist-PPG cohort in the ledger and the only one with expert-scored overnight sleep labels. Collected at Duke University Health System Sleep Disorder Lab, May–Sept 2022.","signals":["ppg","accel","skin-temp","eda","psg"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":100,"license":"PhysioNet Restricted Health Data License 1.5.0","gated":true,"wrist":true,"source_url":"https://physionet.org/content/dreamt/","doi":null,"url":"/research/datasets/dreamt"},{"slug":"e4-stress-exercise","name":"E4 Stress + Exercise (PhysioNet wearable-device-dataset)","summary":"Empatica E4 wristband recordings under three distinct physiological load conditions: acute psychological stress, aerobic exercise, anaerobic exercise. Published June 2025, PhysioNet DOI 10.13026/he0v-tf17, companion paper Scientific Data 12(1) 10.1038/s41597-025-04845-9. Member of e4-commercial-cluster.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":36,"license":"ODC-By 1.0","gated":null,"wrist":true,"source_url":"https://physionet.org/content/wearable-device-dataset/1.0.1/","doi":"10.13026/he0v-tf17","url":"/research/datasets/e4-stress-exercise"},{"slug":"earset","name":"EarSet","summary":null,"signals":["ppg","ecg","accel","gyro","resp"],"ppg_sites":["ear"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":30,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/8142332","doi":"10.5281/zenodo.8142332","url":"/research/datasets/earset"},{"slug":"ecsmp","name":"ECSMP","summary":"Gao et al., Southeast University.","signals":["ppg","ecg","accel","skin-temp","eda","eeg"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":89,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://data.mendeley.com/datasets/vn5nknh3mn/2","doi":"10.17632/vn5nknh3mn.2","url":"/research/datasets/ecsmp"},{"slug":"eevr","name":"EEVR","summary":"Pragya Singh, Ritvik Budhiraja, Ankush Gupta, Anshul Goswami, Mohan Kumar, Pushpendra Singh (IIIT-Delhi; RIT). NeurIPS 2024 Datasets & Benchmarks (OpenReview qgzdGyQcDt). Introduces CLSP (contrastive language–signal pretraining).","signals":["ppg","eda"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":37,"license":null,"gated":true,"wrist":null,"source_url":"https://melangelabiiitd.github.io/EEVR/","doi":null,"url":"/research/datasets/eevr"},{"slug":"emowear","name":"EmoWear (E4 SAM affect)","summary":"Wearable physiological + motion dataset for emotion recognition / context awareness (Scientific Data 2024). Affect-adjacent to stress (SAM valence/arousal/dominance), not a dedicated stress protocol. Zenodo DOI 10.5281/zenodo.10407279.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":48,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/10407279","doi":"10.5281/zenodo.10407279","url":"/research/datasets/emowear"},{"slug":"e4-commercial-cluster","name":"Empatica E4 Commercially-Clear Cluster (CogWear, Exam Stress, ScientISST MOVE, Neuro-Stress-Resilience-HCI, Wrist PPG During Exercise)","summary":"Five separate PhysioNet datasets that individually are too small to matter but share an almost identical sensor layout, so they concatenate into one corpus with a single extraction pipeline.","signals":["ppg","accel","skin-temp","eda","ecg-ref"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"ODC-By 1.0 / ODbL 1.0 / CC BY 4.0","gated":null,"wrist":true,"source_url":"https://physionet.org/about/database/","doi":null,"url":"/research/datasets/e4-commercial-cluster"},{"slug":"empatica-e4-stress","name":"EmpaticaE4Stress (workplace cognitive stress)","summary":"Workplace-style cognitive / social / psychological stress protocol with Empatica E4 (Campanella et al.). Mendeley Data v2, DOI 10.17632/kb42z77m2g.2. Companion Data-in-Brief 2024.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":29,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://data.mendeley.com/datasets/kb42z77m2g/2","doi":"10.17632/kb42z77m2g.2","url":"/research/datasets/empatica-e4-stress"},{"slug":"emteq-breathing-vr","name":"Emteq Breathing Data VR (forehead PPG)","summary":"Kaggle emteqlabs/breathingdatavr. Three sub-datasets (free + controlled breathing rates). Paper: Stankoski et al., Sensors 2022.","signals":["ppg","accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":40,"license":null,"gated":null,"wrist":false,"source_url":"https://www.kaggle.com/datasets/emteqlabs/breathingdatavr","doi":"10.3390/s22062079","url":"/research/datasets/emteq-breathing-vr"},{"slug":"epistress","name":"EPIStress (epilepsy cognitive stress, E4+Muse)","summary":"Multimodal cognitive-stress dataset in epilepsy patients (lab + in-situ). Zenodo DOI 10.5281/zenodo.16407549. Scientific Data descriptor 2025.","signals":["ppg","accel","skin-temp","eda","eeg"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":20,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/16407549","doi":"10.5281/zenodo.16407549","url":"/research/datasets/epistress"},{"slug":"eppg-glucose-stu-zenodo","name":"ePPG glucose dataset (STU Bratislava)","summary":"Berki & Mateašík (Slovak University of Technology / International Laser Center). Zenodo 10.5281/zenodo.21308261 (2026-07-11; later versions 10.5281/zenodo.21978226 and 22206256 exist).","signals":["ppg","accel"],"ppg_sites":["finger"],"ppg_wavelengths_nm":"ir 940 / red 655 nm","bp_reference":[],"subjects":24,"license":"CC0 1.0","gated":null,"wrist":false,"source_url":"https://doi.org/10.5281/zenodo.21308261","doi":"10.5281/zenodo.21308261","url":"/research/datasets/eppg-glucose-stu-zenodo"},{"slug":"escolar","name":"ESCOLAR","summary":"ESCOLAR integrates two domains on EmotiBit: social stress in naturalistic academic presentations and controlled programming cognitive-load tasks. Zenodo 19495742, DOI 10.5281/zenodo.19495742, license CC BY 4.0. >1.2M synchronized instances claimed; includes processing scripts.","signals":["ppg","accel","gyro","skin-temp","eda"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":26,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/19495742","doi":"10.5281/zenodo.19495742","url":"/research/datasets/escolar"},{"slug":"eviora-har","name":"EVIORA-HAR","summary":"EVIORA-HAR (Elderly Multi-View and Wearable Motion Recognition Dataset) from Iwan Kurnianto Wibowo, Chastine Fatichah, and Nanik Suciati (Institut Teknologi Sepuluh Nopember / PENS). Zenodo mirror https://zenodo.org/records/22239523 carries IEEE DataPort DOI 10.21227/2zvc-nb24 (publication date on record 2026-08-09; Zenodo created 2026-09-02). Companion Data in Brief article DOI 10.1016/j.dib.2026.113285 (PMC13654039; PubMed 42860996).","signals":["accel","gyro"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":104,"license":"CC BY-NC 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/22239523","doi":"10.21227/2zvc-nb24","url":"/research/datasets/eviora-har"},{"slug":"falltl","name":"FallTL","summary":"Inertial sensors on eight body locations; 12 ADL types and 34 fall types; ~28.43 h total; ready-to-use fall labels. Zenodo, CC BY 4.0, 1 file, 0.403 GB. Subject count not stated on the record (Awesome-IMU-Sensing lists 45).","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://zenodo.org/records/17552449","doi":null,"url":"/research/datasets/falltl"},{"slug":"finger-contact-ecg-dual-wavelength-ppg-post-exercise","name":"Finger-contact ECG + dual-wavelength (660/940 nm) PPG, rest + post-exercise recovery (n=148, 444 records)","summary":"Missed: S2/OpenAlex never run (MDPI Data not in arXiv/PubMed filters).","signals":["ppg","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":148,"license":null,"gated":null,"wrist":false,"source_url":"https://doi.org/10.3390/data11070155","doi":"10.3390/data11070155","url":"/research/datasets/finger-contact-ecg-dual-wavelength-ppg-post-exercise"},{"slug":"galaxy-active2-mental-health","name":"Galaxy Watch Active2 raw PPG+ACC (PHQ-9 / GAD-7)","summary":"Four-week in-situ wearable HRV dataset with sleep diaries and biweekly clinical questionnaires (Scientific Data 2025). Figshare DOI 10.6084/m9.figshare.28509740.","signals":["ppg","accel"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://doi.org/10.6084/m9.figshare.28509740","doi":null,"url":"/research/datasets/galaxy-active2-mental-health"},{"slug":"galaxyppg","name":"GalaxyPPG","summary":"Semi-naturalistic wrist PPG dataset collected at KAIST with a consumer smartwatch and a research-grade wristband recording simultaneously, plus a chest ECG reference. Published as a Scientific Data descriptor (2025), DOI 10.1038/s41597-025-05152-z.","signals":["ppg","ecg","accel","skin-temp"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":24,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://doi.org/10.5281/zenodo.14635823","doi":"10.5281/zenodo.14635823","url":"/research/datasets/galaxyppg"},{"slug":"groupaffect4","name":"GroupAffect-4","summary":"GroupAffect-4 is a multimodal corpus of co-located four-person collaboration (T0 rest → T1 hidden-profile → T2 negotiation → T3 ideation → T4 public-goods). Creators include Jamshidi Seikavandi et al. Full release ~30 GB (Zenodo 20037847); subset release without raw audio (Zenodo 20809799, recommended start).","signals":["ppg","accel","skin-temp","eda","audio","eye-tracking"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":40,"license":"CC BY 4.0 (tabular, physiological and eye-tracking data); raw audio restricted under a data use agreement","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/20809799","doi":"10.5281/zenodo.20809799","url":"/research/datasets/groupaffect4"},{"slug":"gyro-acc-ppg-bami1","name":"Gyro-Acc-PPG (BAMI-I, Wonkwang)","summary":"Hooseok Lee, Heewon Chung, Jinseok Lee (BAMI Lab, Wonkwang University). Paper: \"Motion Artifact Cancellation in Wearable Photoplethysmography Using Gyroscope\", IEEE Sensors Journal, doi 10.1109/JSEN.2018.2879970. GitHub repo created 2018-10-05; 24 Subject.mat files.","signals":["ppg","accel","gyro","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":24,"license":"Copyright retained; free for the Signal Processing Cup or academic research with citation","gated":null,"wrist":true,"source_url":"https://github.com/hooseok/gyro_acc_ppg","doi":"10.1109/JSEN.2018.2879970","url":"/research/datasets/gyro-acc-ppg-bami1"},{"slug":"har70plus","name":"HAR70+","summary":"18 fit-to-frail older adults aged 70-95 (5 used walking aids), two Axivity AX3 accelerometers (right thigh, lower back), ~40 min semi-structured free-living, 50 Hz, video-annotated frame-by-frame. UCI, CC BY 4.0.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":18,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://archive.ics.uci.edu/dataset/780/har70","doi":null,"url":"/research/datasets/har70plus"},{"slug":"harmes","name":"HARMES","summary":"HARMES (Burchard / Brückner / Bock / Van Laerhoven et al.) — multi-modal wearable HAR with motion, environmental sensing, and sound. Zenodo 10.5281/zenodo.19425719; arXiv 2605.02596; code https://github.com/RBurchard/HARMES. Raw + 50 Hz preprocessed pickles.","signals":["accel","gyro","audio","atmospheric-temp","humidity","pressure"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":20,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/19425719","doi":"10.5281/zenodo.19425719","url":"/research/datasets/harmes"},{"slug":"harth","name":"HARTH","summary":"22 subjects wearing two Axivity AX3 3-axis accelerometers (right front thigh, lower back) for ~2 h free-living; 50 Hz; frame-by-frame labels from chest-mounted video. One CSV per subject. UCI, CC BY 4.0.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":22,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://archive.ics.uci.edu/dataset/779/harth","doi":null,"url":"/research/datasets/harth"},{"slug":"healthring","name":"HealthRing RingDatasetV2.1","summary":"Tang, Wang, Ding, Ji, Wang, Wang, Zhang, Chen, Gao, Shi, Wang. HealthRing: Physiology Dataset for Health Sensing on Rings. Sci Data (2026), doi:10.1038/s41597-026-07289-x.","signals":["ppg","accel","ecg","resp"],"ppg_sites":["finger","ring"],"ppg_wavelengths_nm":null,"bp_reference":["cuff"],"subjects":54,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/18426864","doi":"10.5281/zenodo.18426864","url":"/research/datasets/healthring"},{"slug":"heart-watch","name":"HEART-Watch","summary":null,"signals":["ppg","ecg","accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":40,"license":null,"gated":true,"wrist":true,"source_url":"https://doi.org/10.1371/journal.pdig.0001647","doi":"10.1371/journal.pdig.0001647","url":"/research/datasets/heart-watch"},{"slug":"heartcycle","name":"HeartCycle: A comprehensive dataset of synchronized impedance cardiography and echocardiography for accurate hemodynamic predictions","summary":null,"signals":["ppg","ecg"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["cuff"],"subjects":null,"license":"ODC-By 1.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/heartcycle/1.0.0/","doi":"10.13026/z865-eb23","url":"/research/datasets/heartcycle"},{"slug":"hhar","name":"HHAR","summary":"Accelerometer + gyroscope from multiple smartphone and smartwatch models while users performed biking, sitting, standing, walking, stairs up/down in no specific order. UCI, CC BY 4.0. 43.9M instances.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":9,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://archive.ics.uci.edu/dataset/344/heterogeneity+activity+recognition","doi":null,"url":"/research/datasets/hhar"},{"slug":"human-sleep-project","name":"Human Sleep Project (BDSP)","summary":"Restricted BDSP dataset; access needs BDSP credentialing plus the Credentialed Health DUA. v3.0 published 2026-06-02 (Li, Wen, Sun, Ganglberger, … Westover), DOI 10.60508/m3sw-rz13; earlier v2.0 DOI 10.60508/qjbv-hg78 (cited by AnyPPG). Data in BIDS layout with HDF5 signals, linked to de-identified demographics and EHR (22 ICD-10 categories).","signals":["psg","ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":90000,"license":"BDSP Credentialed Health Data Use Agreement; noncommercial bona fide research only","gated":true,"wrist":false,"source_url":"https://bdsp.io/content/hsp/3.0/","doi":"10.60508/m3sw-rz13","url":"/research/datasets/human-sleep-project"},{"slug":"icbhi-2017","name":"ICBHI 2017 Respiratory Sound Database","summary":"920 annotated auscultation recordings (10–90 s) from 126 subjects, 5.5 h, 6,898 respiratory cycles (1,864 crackles, 886 wheezes, 506 both). Source: official ICBHI challenge page (via search, 2026-10-12).","signals":["audio"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":126,"license":"No formal license; \"freely available for research\" (official site)","gated":null,"wrist":false,"source_url":"https://bhichallenge.med.auth.gr/ICBHI_2017_Challenge","doi":"10.1088/1361-6579/ab03ea","url":"/research/datasets/icbhi-2017"},{"slug":"ieee-spc-2015-troika","name":"IEEE Signal Processing Cup 2015 (TROIKA)","summary":"Zhang et al. (TROIKA; arXiv 1409.5181). Used for the IEEE SPC 2015 heart-rate challenge.","signals":["ppg","accel","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":12,"license":"Original: copyright, academic research only (prior INDEX reject note); Zenodo IEEEPPG repackaging labelled CC BY 4.0","gated":null,"wrist":true,"source_url":"https://signalprocessingsociety.org/community-involvement/ieee-signal-processing-cup-2015","doi":"10.5281/zenodo.3902710","url":"/research/datasets/ieee-spc-2015-troika"},{"slug":"imu50","name":"IMU50","summary":"IMU50 (La Fratta / Calisti / Bigelli et al.): multiday high-resolution wrist IMU+PPG under unrestricted free-living. Zenodo concept DOI 10.5281/zenodo.21468409 (version DOI 10.5281/zenodo.21468410). Intended for (1) self-supervised IMU representation learning and (2) supervised metabolic / HR / morphology tasks.","signals":["ppg","accel","gyro","skin-temp"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":50,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/21468409","doi":"10.5281/zenodo.21468410","url":"/research/datasets/imu50"},{"slug":"infrared-bp-tomstog","name":"Infrared-BP","summary":"Infrared-camera videos of forehead and upper palm; Eulerian video magnification then PTT estimation fitted to reference BP. Papers: CNNA 2023 and MDPI BioMedInformatics 4(1):25 (2024). Videos hosted on MEGA.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":null,"gated":null,"wrist":null,"source_url":"https://github.com/TomStog/Infrared-BP","doi":null,"url":"/research/datasets/infrared-bp-tomstog"},{"slug":"iomt-secalarmbench","name":"IoMT-SecAlarmBench (semi-synthetic ECG+PPG attack benchmark)","summary":"GitHub techosystem/IoMTSecAlarmBench. getdata.sh pulls CinC 2015. 78,729 labelled 10 s windows.","signals":["ppg","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"MIT (benchmark code); ODC-By 1.0 (CinC 2015 waveforms)","gated":null,"wrist":false,"source_url":"https://github.com/techosystem/IoMT_SecAlarmBench","doi":null,"url":"/research/datasets/iomt-secalarmbench"},{"slug":"ipqm-fall","name":"IPqM-Fall","summary":"IPqM-Fall from the Navy Research Institute (IPqM) “Future Combatant” project with CEFET-RJ. Zenodo 10.5281/zenodo.12760391. Companion paper: arXiv 2501.15655.","signals":["accel","gyro"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":15,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/12760391","doi":"10.5281/zenodo.12760391","url":"/research/datasets/ipqm-fall"},{"slug":"iridia-af","name":"IRIDIA-AF","summary":"Gilon et al., Scientific Data companion release (Nature Sci Data article linked from Zenodo). Zenodo record 8405941 v1.0.1 (accessright=open, license.id=cc-by-4.0). GitHub: cedricgilon/iridia-af.","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":152,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/8405941","doi":"10.5281/zenodo.8405941","url":"/research/datasets/iridia-af"},{"slug":"ispl-imu-stretch","name":"iSPL IMU-Stretch","summary":"One subject wearing 3 IMUs (right wrist, waist, ankle) and 2 fabric stretch sensors at the knees (ICAIIC 2022). No licence file.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":1,"license":"none in repo (all rights reserved by default)","gated":null,"wrist":null,"source_url":"https://github.com/thunguyenth/HAR_IMU_Stretch","doi":null,"url":"/research/datasets/ispl-imu-stretch"},{"slug":"k-emocon","name":"K-EmoCon","summary":"Park et al., Scientific Data 7:293 (2020). Zenodo record v1.0.0 DOI 10.5281/zenodo.3931963 (files restricted; user agreement https://forms.gle/DUAERhHqf51kyt4Y9). KAIST IRB.","signals":["ppg","accel","eda","skin-temp","ecg","eeg","hr"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":32,"license":"Restricted access (request on Zenodo); no license listed","gated":true,"wrist":true,"source_url":"https://zenodo.org/records/3931963","doi":"10.5281/zenodo.3931963","url":"/research/datasets/k-emocon"},{"slug":"kingston-icu-af","name":"Kingston ICU AF","summary":"Nassar / Maghsoodi / Chen / Maslove / Sibley / Laird / Mousavi (Queen’s / KHSC). Archival ICU monitors 2015–2020; one random 10 s strip per labelled patient. Companion TBME 2026 benchmarks: 10.1109/TBME.2026.3715145.","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":596,"license":"PhysioNet Restricted Health Data License 1.5.0","gated":true,"wrist":false,"source_url":"https://physionet.org/content/kingston-icu-af/1.0.0/","doi":"10.13026/47wc-qk15","url":"/research/datasets/kingston-icu-af"},{"slug":"landry-adl-bp","name":"Landry ADL","summary":"University of Waterloo (Landry, Hedge, Hughson, Peterson, Arami). Collected to validate a cuffless BP model during ADL. Paper: \"Accurate Blood Pressure Estimation during Activities of Daily Living: A Wearable Cuffless Solution\", IEEE J-BHI 2021.","signals":["ppg","ecg","accel","abp"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":["finapres"],"subjects":5,"license":"CC BY 4.0 + IEEE DataPort Terms of Use","gated":null,"wrist":false,"source_url":"https://ieee-dataport.org/documents/wearable-physiological-and-blood-pressure-measurements-during-activities-daily-living","doi":"10.21227/wysp-gt69","url":"/research/datasets/landry-adl-bp"},{"slug":"lara-logistics","name":"LARa v03","summary":"TU Dortmund logistics lab: two picking and one packing scenario, 16 subjects, optical marker MoCap, IMUs and RGB camera; each subject recorded 1 h (960 min total); 8 activity classes + 19 attributes. Zenodo, CC BY-NC 4.0, 12 files, 42.7 GB.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":16,"license":"CC BY-NC 4.0","gated":null,"wrist":null,"source_url":"https://zenodo.org/records/8189341","doi":null,"url":"/research/datasets/lara-logistics"},{"slug":"lightweight-bp-xt2j","name":"Lightweight BP Estimation (Yongjian Li)","summary":"Uploaded by Yongjian Li (2024-06-07). No paper link, no README in the files.","signals":["ppg","ecg","abp"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line","cuff"],"subjects":300,"license":"CC BY 4.0 + IEEE DataPort Terms of Use","gated":null,"wrist":false,"source_url":"https://ieee-dataport.org/documents/lightweight-blood-pressure-estimation-algorithm","doi":"10.21227/xt2j-5t27","url":"/research/datasets/lightweight-bp-xt2j"},{"slug":"loneliness-fi","name":"Loneliness-FI","summary":"Dryad dataset DOI 10.5061/dryad.qz612jmrn — Finnish immigrants loneliness wearable multimodal study.","signals":["ppg","accel","gyro"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":39,"license":"CC0 1.0","gated":null,"wrist":true,"source_url":"https://datadryad.org/dataset/doi:10.5061/dryad.qz612jmrn","doi":"10.5061/dryad.qz612jmrn","url":"/research/datasets/loneliness-fi"},{"slug":"long-term-af-ppg-ecg-bacevicius-2024","name":"Long-term ECG + wrist PPG with annotated AF episodes (Bacevičius 2024, Zenodo)","summary":"45 suspected-AF patients monitored 5–8 days (306 days total) with a KTU wrist-worn PPG device and a Bittium Faros 180 ECG patch; beat-level AF annotations, QRS indices and RR intervals; subjectinfo.xlsx with demographics/comorbidities. v2 (2024-06-01); v1 (record 5815074) had 8 patients. Files restricted (Zenodo request).","signals":["ppg","ecg","accel"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":45,"license":"Zenodo \"other-nc\" (restricted access)","gated":true,"wrist":true,"source_url":"https://zenodo.org/records/11242869","doi":"10.5281/zenodo.11242869","url":"/research/datasets/long-term-af-ppg-ecg-bacevicius-2024"},{"slug":"afppgecg","name":"Long-term ECG and wrist PPG with annotated AF episodes (AFPPGECG)","summary":"45 patients with suspected atrial fibrillation, monitored 5–8 days each (306 monitoring days total), starting at Vilnius University Hospital and continuing at home. Wrist PPG device running continuously alongside a clinical ECG patch. Zenodo record v2, June 2024 (DOI 10.5281/zenodo.11242869).","signals":["ppg","ecg","accel"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":39,"license":"Other (non-commercial), as listed on Zenodo","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/23187681","doi":"10.5281/zenodo.23187681","url":"/research/datasets/afppgecg"},{"slug":"lown2020-polar-af","name":"Lown 2020 Polar H7","summary":"Supporting files for Lown et al., PLOS ONE (10.1371/journal.pone.0227401): Excel labels (pone.0227401.s001.xlsx), RR .dat archive (s002.7z), and methods docx (s003.docx).","signals":["rr-interval"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":415,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://doi.org/10.1371/journal.pone.0227401","doi":"10.1371/journal.pone.0227401","url":"/research/datasets/lown2020-polar-af"},{"slug":"maibo","name":"MAIBO","summary":"MAIBO (Chen / Li / Liu / Wang / Shi / Gao / Qiu / Tu / Wang / Wang / Dai / Chen; Shanghai Jiao Tong University + Mianyang Central Hospital). Zenodo record 19440421 (published 2026-04-06, V1). Companion Sci Data descriptor: A Multi-posture Asymmetry-aware Intelligent Bilateral Observation Dataset for Cardiovascular Monitoring (doi:10.1038/s41597-026-07915-8).","signals":["ppg"],"ppg_sites":["finger","ring"],"ppg_wavelengths_nm":null,"bp_reference":["cuff"],"subjects":1086,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/19440421","doi":"10.5281/zenodo.19440421","url":"/research/datasets/maibo"},{"slug":"maus-mental-workload","name":"MAUS","summary":"MAUS (Mental workloAd assessment Under n-back task using wearable Sensors) is a multimodal physiological dataset for mental workload (MW) assessment. Subjects perform an N-back working-memory task while clinical-grade and consumer wrist sensors record in parallel. Authors: Beh / Wu / Wu (NTU).","signals":["ecg","eda","ibi","finger-ppg","wrist-ppg"],"ppg_sites":["finger","wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":22,"license":"CC BY 4.0 + IEEE DataPort Terms of Use","gated":null,"wrist":true,"source_url":"https://ieee-dataport.org/open-access/maus-dataset-mental-workload-assessment-n-back-task-using-wearable-sensor","doi":"10.21227/q4td-yd35","url":"/research/datasets/maus-mental-workload"},{"slug":"mbb-avr-affective-vr-multimodal","name":"MBB-AVR","summary":null,"signals":["eeg","ecg","ppg","eda","resp","eye-tracking"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":47,"license":null,"gated":null,"wrist":null,"source_url":"https://doi.org/10.31234/osf.io/vg2qc_v1","doi":"10.31234/osf.io/vg2qc_v1","url":"/research/datasets/mbb-avr-affective-vr-multimodal"},{"slug":"mcphases-menstrual-wearable","name":"mcphases-menstrual-wearable","summary":"42 Canadian young adults, two 3-month intervals, Fitbit Sense + Dexcom G6 CGM + Mira Plus urinary hormone analyzer + daily symptom diary. Published via PhysioNet 2025-09-09, University of Toronto REB-approved.","signals":["skin-temp","spo2","hrv","fitbit-derived heart_rate","sleep_stages","respiratory_rate","activity","vo2max","stress_score","continuous_glucose","urinary_hormones"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":null,"gated":null,"wrist":null,"source_url":"https://physionet.org/content/mcphases/","doi":null,"url":"/research/datasets/mcphases-menstrual-wearable"},{"slug":"mefar","name":"MEFAR","summary":"Workplace / occupational mental fatigue multimodal set released with Data in Brief (2023/2024). Morning and evening sessions on the same workers; Chalder Fatigue Scale labels paired with Empatica E4 waveforms and single-channel EEG. Mendeley DOI 10.17632/z3g26tphnv.5; DiB DOI 10.1016/j.dib.2023.109896.","signals":["ppg","accel","skin-temp","eda","eeg"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":23,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://data.mendeley.com/datasets/z3g26tphnv/5","doi":"10.17632/z3g26tphnv.5","url":"/research/datasets/mefar"},{"slug":"mesa","name":"MESA Sleep","summary":"Multi-Ethnic Study of Atherosclerosis: 6,814 Black, White, Hispanic and Chinese-American adults aged 45–84 at baseline. The sleep ancillary study is distributed by NSRR with PSG and actigraphy raw data plus covariates.","signals":["psg","ppg","ecg","eeg","resp","accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":2237,"license":"NSRR data use agreement; \"MESA has restrictions for commercial purposes\"","gated":true,"wrist":false,"source_url":"https://sleepdata.org/datasets/mesa","doi":"10.25822/n7hq-c406","url":"/research/datasets/mesa"},{"slug":"mfd-multimodal-driver-fatigue","name":"MFD","summary":null,"signals":["eeg","ecg","emg","eog","ppg","bcg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":19,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://ieee-dataport.org/documents/mfd-multimodal-driver-fatigue","doi":"10.21227/25yw-mc95","url":"/research/datasets/mfd-multimodal-driver-fatigue"},{"slug":"mgmcet-fmci-pbf","name":"MGMCET-FMCI-PBF","summary":"Four studies (page text): Study-1 wrist IPG (~200 young volunteers, 14,478 records), Study-2 wrist IPG (normals + hypertensives, 1,029 records), Study-3 finger PPG (73 records), Study-4 ECG + finger PPG (page: 51 normal + 22 hypertensive). Studies 1–3 not downloaded.","signals":["ecg","ppg"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":73,"license":null,"gated":null,"wrist":false,"source_url":"https://ieee-dataport.org/documents/mgmcet-fmci-pbf-peripheral-blood-flow-database-using-ipg-ppg-and-ecg","doi":"10.21227/rbwb-zx09","url":"/research/datasets/mgmcet-fmci-pbf"},{"slug":"mhealth","name":"mhealth","summary":null,"signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":10,"license":null,"gated":null,"wrist":null,"source_url":null,"doi":"10.24432/C5TW22","url":"/research/datasets/mhealth"},{"slug":"mimic-perform","name":"MIMIC PERform","summary":"Charlton PERform family used in PPG beat-detector benchmarking (Physiol. Meas. 2022, DOI 10.1088/1361-6579/ac826d).","signals":["ppg","ecg","resp","abp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":35,"license":"ODbL","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/15906524","doi":"10.5281/zenodo.15906524","url":"/research/datasets/mimic-perform"},{"slug":"mimic-bp","name":"MIMIC-BP","summary":"Harvard Dataverse doi:10.7910/DVN/DBM1NF (version 2.2, released 2024-11-21), with Samsung R&D Institute Brazil and Otávio A. B. Penatti among the authors.","signals":["ppg","abp","ecg","resp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":1524,"license":"ODbL 1.0","gated":null,"wrist":false,"source_url":"https://doi.org/10.7910/DVN/DBM1NF","doi":"10.7910/DVN/DBM1NF","url":"/research/datasets/mimic-bp"},{"slug":"mimic-matched-bp","name":"MIMIC-III Waveform MATCHED","summary":null,"signals":["ppg","abp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":2854,"license":"ODbL 1.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/mimic3wdb-matched/1.0/","doi":null,"url":"/research/datasets/mimic-matched-bp"},{"slug":"mimic-iii-ext-ca","name":"MIMIC-III-Ext-CA","summary":"MIMIC-III-Ext-CA (Hup / Long / Vullings). PhysioNet v1.0.0 published 2026-03-10. DOI 10.13026/ec0n-y377.","signals":["ppg","ecg"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":31,"license":"PhysioNet Credentialed Health Data License 1.5.0","gated":true,"wrist":false,"source_url":"https://physionet.org/content/mimic-iii-ext-ca/1.0.0/","doi":"10.13026/ec0n-y377","url":"/research/datasets/mimic-iii-ext-ca"},{"slug":"mimic-iii-ext-ppg","name":"MIMIC-III-Ext-PPG","summary":"Curated extract from MIMIC-III Waveform Database Matched Subset (Moulaeifard / Charlton / Strodthoff). Segments end at rhythm chart events (≤15 min lookback). Sci Data companion: 10.1038/s41597-026-07335-8.","signals":["ppg","ecg","resp","abp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":6189,"license":"PhysioNet Credentialed Health Data License 1.5.0","gated":true,"wrist":false,"source_url":"https://physionet.org/content/mimic-iii-ext-ppg/1.1.0/","doi":"10.13026/r6k1-xt76","url":"/research/datasets/mimic-iii-ext-ppg"},{"slug":"mimic-iv-waveform","name":"MIMIC-IV Waveform Database (PhysioNet preview)","summary":"PhysioNet mimic4wdb 0.1.0 technical preview. WFDB+FLAC. Linked to MIMIC-IV clinical via subjectid/hadmid.","signals":["ppg","ecg","resp","abp"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":198,"license":"ODbL 1.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/mimic4wdb/0.1.0/","doi":null,"url":"/research/datasets/mimic-iv-waveform"},{"slug":"mimic-iv-ext-ecg-glucose","name":"MIMIC-IV-Ext-ECG-Glucose: Temporally Aligned 12-Lead ECG and Laboratory Blood Glucose Pairs with Quality and Pharmacological Annotations","summary":"A derived table built by linking every MIMIC-IV laboratory blood glucose to the nearest 12-lead diagnostic ECG from MIMIC-IV-ECG recorded within ±240 min. Records come from BIDMC ED, inpatient, and ICU encounters. Files: mimicivecgglucosealigned.csv (437,671 rows × 101 columns), datadictionary.csv, the BigQuery generation SQL, and a README.","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"PhysioNet Credentialed Health Data License 1.5.0","gated":null,"wrist":null,"source_url":null,"doi":"10.13026/nts2-vj76","url":"/research/datasets/mimic-iv-ext-ecg-glucose"},{"slug":"mimic3-af-bashar","name":"MIMIC3-AF-Bashar","summary":"Annotations originate from Bashar et al. (JBHI / UConn) Figshare 10.6084/m9.figshare.12149091.v1 (public AF labels for MIMIC-III matched sepsis ECG review).","signals":["ppg","ecg","resp","abp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":70,"license":"ODbL (waveforms); CC BY 4.0 (annotations)","gated":null,"wrist":false,"source_url":"https://doi.org/10.6084/m9.figshare.12149091.v1","doi":"10.6084/m9.figshare.12149091.v1","url":"/research/datasets/mimic3-af-bashar"},{"slug":"mission1bp-gmu-kime","name":"MISSION1BP-GMU-KIME","summary":"MISSION1BP–GMU-KIME (Y. Zhang et al.; companion preprint DOI 10.21203/rs.3.rs-9176759/v1). Zenodo record 21974809, published 2026-08-17.","signals":["ppg","ecg","accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":1000,"license":"Custom data use agreement (MISSoN-BP access request form)","gated":true,"wrist":null,"source_url":"https://zenodo.org/records/21974809","doi":"10.5281/zenodo.21974809","url":"/research/datasets/mission1bp-gmu-kime"},{"slug":"moods-field-stress-fossil","name":"MOODS","summary":"Neupane et al., \"Momentary Stressor Logging and Reflective Visualizations\", CHI '24 (DOI 10.1145/3613904.3642662; arXiv 2401.16307). Participants wore a Fossil Sport WearOS watch for 100 days; the watch triggered stress prompts when physiology suggested stress, and participants logged stressors and rated stress. IRB approved.","signals":["ppg","accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":122,"license":"Private. University of Memphis IRB-approved study; Pulse-PPG states the training data are private and not publicly available.","gated":true,"wrist":true,"source_url":"https://doi.org/10.1145/3613904.3642662","doi":"10.1145/3613904.3642662","url":"/research/datasets/moods-field-stress-fossil"},{"slug":"sleep-accel","name":"Motion and heart rate from a wrist-worn wearable and labeled sleep from polysomnography","summary":null,"signals":["accel","hr"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"ODC-By 1.0","gated":null,"wrist":true,"source_url":"https://physionet.org/content/sleep-accel/1.0.0/","doi":"10.13026/hmhs-py35","url":"/research/datasets/sleep-accel"},{"slug":"mpmrd-multi-pace-running","name":"MPMRD","summary":"Treadmill running from 30 healthy runners (8 female, 22 male). Each did five incremental relative-pace trials prescribed from individual VDOT, about 3 min per pace. Synchronized: VICON optical motion capture, COSMED PLUTO instrumented treadmill (kinetics), COSMED K5 portable metabolic analyzer, Polar Verity Sense optical HR sensor, HUAWEI GT Runner smartwatch, and Stryd Duo foot pods.","signals":["hr"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":30,"license":null,"gated":true,"wrist":true,"source_url":"https://zenodo.org/records/23098209","doi":"10.5281/zenodo.23098209","url":"/research/datasets/mpmrd-multi-pace-running"},{"slug":"mpu-rppg","name":"MPU-rPPG","summary":"Chen, Tan, Yu, Jiang, Wang, Gao, Sun (Macao Polytechnic University, Great Bay University). Sci Data 13:1216 (2026), DOI 10.1038/s41597-026-07310-3, PMC13500808. Ethics: Zhuhai People's Hospital ZH20250306, written consent.","signals":["video","ppg","spo2"],"ppg_sites":["face-camera","finger"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":50,"license":"Sample subset CC BY 4.0 (Figshare); full release pending; article CC BY-NC-ND 4.0","gated":null,"wrist":false,"source_url":"https://doi.org/10.6084/m9.figshare.29377835","doi":"10.1038/s41597-026-07310-3","url":"/research/datasets/mpu-rppg"},{"slug":"multisite-ppg","name":"Multi-site PPG (Earring / Necklace / Ring / Watch)","summary":"In-the-wild physiological dataset from four custom wearables worn simultaneously at earring, necklace, ring, and watch (wrist) positions, with a Polar H10 chest ECG reference. arXiv 2026 (arXiv:2605.17859), companion paper under review.","signals":["ppg","ecg","accel","skin-temp"],"ppg_sites":["ear","neck","ring","wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY-NC 4.0","gated":null,"wrist":true,"source_url":"https://huggingface.co/datasets/snowballlab/Multisite-PPG","doi":null,"url":"/research/datasets/multisite-ppg"},{"slug":"multimodal-micromobility-stress","name":"Multimodal Micromobility Stress Dataset","summary":"Figshare article 31868863 (v2), DOI 10.6084/m9.figshare.31868863.v2, authored by Sai Sree Nithya Gattineni. Published 2026-10-05. Title: Multimodal Micromobility Stress Dataset.","signals":["ppg","eda","skin-temp","accel"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://figshare.com/articles/dataset/Multimodal_Micromobility_Stress_Dataset/31868863","doi":"10.6084/m9.figshare.31868863","url":"/research/datasets/multimodal-micromobility-stress"},{"slug":"muse-2-forehead-ppg-polar-validity","name":"Muse 2 forehead PPG vs Polar H10 concurrent validity (HR / ultra-short RMSSD)","summary":"Griadhi, Suadnyana, Wahyuni (Universitas Dhyana Pura). Title on the record: Data and code for: Muse 2 forehead photoplethysmography agrees with Polar H10 for heart rate but overestimates ultra-short RMSSD. Ethics: Universitas Dhyana Pura 006821/KEP/2026.","signals":["ppg"],"ppg_sites":["forehead"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":28,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/23091944","doi":"10.5281/zenodo.23091944","url":"/research/datasets/muse-2-forehead-ppg-polar-validity"},{"slug":"mw-ppg-hr","name":"MW-PPG-HR","summary":"Manchester Metropolitan University benchmark for PPG-based HR estimation, published Figshare 2026-03-05. Explicitly built to fix cohort-diversity and wavelength gaps in existing resources. Companion paper: \"Towards fair and trustworthy heart rate estimation from wrist-worn photoplethysmography: a multi-wavelength dataset and uncertainty-aware deep learning approach evaluated across skin tones, sexes and motion conditions.\"","signals":["ppg","ecg","accel"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":"ir 850 / red 660 nm","bp_reference":[],"subjects":20,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://doi.org/10.6084/m9.figshare.30017974","doi":"10.6084/m9.figshare.30017974","url":"/research/datasets/mw-ppg-hr"},{"slug":"mymove","name":"MyMove","summary":"MyMove (Kim / Choe / Kacorri et al., CHI 2022 DOI 10.1145/3491102.3517457; labeling pipeline IMWUT 2025). Project page: https://mymove-collective.github.io/dataset/. ISWC 2026 paper footnote: privacy-preserving public subset planned through that page — not downloadable as of 2026-10-05.","signals":["accel","magnetometer","hr"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":13,"license":null,"gated":true,"wrist":true,"source_url":"https://mymove-collective.github.io/dataset/","doi":"10.1145/3491102.3517457","url":"/research/datasets/mymove"},{"slug":"neurobiosense","name":"NeuroBioSense","summary":"Kocaçinar et al., Istanbul Kultur Univ. Published Aug 2023.","signals":["ppg","skin-temp","eda"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":58,"license":null,"gated":null,"wrist":true,"source_url":"https://data.mendeley.com/datasets/7md7yty9sk/1","doi":"10.17632/7md7yty9sk.1","url":"/research/datasets/neurobiosense"},{"slug":"nfi-fared","name":"NFI_FARED","summary":"Netherlands Forensic Institute (Hi-OSCAR, doi:10.1145/3770681): accelerometer, gyroscope and air pressure from 14 participants (8 M, 6 F), age 26.6 ± 8.8; IMUs on dominant wrist and back; files split into controlled (final) and free-living sessions.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":14,"license":null,"gated":null,"wrist":null,"source_url":"https://huggingface.co/datasets/NetherlandsForensicInstitute/NFI_FARED_IMU","doi":null,"url":"/research/datasets/nfi-fared"},{"slug":"nhanes-accelerometer","name":"nhanes-accelerometer","summary":"CDC/NCHS National Health and Nutrition Examination Survey wrist-worn ActiGraph GT3X+ accelerometer data, 2011-2014 cycles, raw 80 Hz. Roughly 11,000 participants wore the device for up to 7 days continuously.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":null,"gated":null,"wrist":null,"source_url":"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2011/DataFiles/PAX80_G.htm","doi":null,"url":"/research/datasets/nhanes-accelerometer"},{"slug":"numom2b","name":"nuMoM2b","summary":"NICHD/NHLBI nuMoM2b, 2011–2013; NSRR distributes core data and home sleep testing raw data plus scoring.","signals":["psg","ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":3702,"license":"NSRR data use agreement","gated":true,"wrist":false,"source_url":"https://sleepdata.org/datasets/numom2b","doi":null,"url":"/research/datasets/numom2b"},{"slug":"nurse-hospital-stress","name":"NURSE (hospital stress, E4, Dryad)","summary":"Multi-modal continuous stress detection of nurses in a real hospital during COVID-19. Dryad DOI 10.5061/dryad.5hqbzkh6f. ~1,250 hours across two 2020 sessions.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":15,"license":"CC0 1.0","gated":null,"wrist":true,"source_url":"https://datadryad.org/dataset/doi:10.5061/dryad.5hqbzkh6f","doi":"10.5061/dryad.5hqbzkh6f","url":"/research/datasets/nurse-hospital-stress"},{"slug":"office-everyday-mwl","name":"Office Everyday MWL (MW_OFFICE, n=10 E4)","summary":"~80 h mental-workload / stress recordings focused on typical office activities (lab + in-the-wild). Zenodo DOI 10.5281/zenodo.15681263. Code: https://github.com/HPI-CH/mwoffice2025","signals":["ppg","accel","skin-temp","eda","eeg"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":10,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/15681263","doi":"10.5281/zenodo.15681263","url":"/research/datasets/office-everyday-mwl"},{"slug":"openneuro-ds007537","name":"OpenNeuro ds007537","summary":"OpenNeuro ds007537 (IIT Delhi). PPG in physio.tsv.gz.","signals":["ppg","eda","eeg","eye-tracking"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":23,"license":"CC0 1.0","gated":null,"wrist":false,"source_url":"https://openneuro.org/datasets/ds007537","doi":"10.18112/openneuro.ds007537.v1.0.0","url":"/research/datasets/openneuro-ds007537"},{"slug":"openoximetry","name":"OpenOximetry Repository (PhysioNet Restricted)","summary":"PhysioNet openox-repo v1.1.1. Goal: study oximeter bias across skin tones.","signals":["ppg","ecg","spo2","abp","etco2"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"PhysioNet Restricted Health Data License 1.5.0","gated":true,"wrist":false,"source_url":"https://physionet.org/content/openox-repo/1.1.1/","doi":null,"url":"/research/datasets/openoximetry"},{"slug":"openwatch-smartwatch-gesture-imu-ppg","name":"OpenWatch","summary":"Missed: window gap on arXiv dataset/benchmark terms.","signals":["accel","gyro","ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":50,"license":null,"gated":null,"wrist":true,"source_url":"https://arxiv.org/abs/2605.04791","doi":"10.48550/arXiv.2605.04791","url":"/research/datasets/openwatch-smartwatch-gesture-imu-ppg"},{"slug":"opportunity","name":"opportunity","summary":null,"signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":4,"license":null,"gated":null,"wrist":null,"source_url":null,"doi":"10.24432/C5M027","url":"/research/datasets/opportunity"},{"slug":"osasud","name":"OSASUD","summary":"OSASUD (Bernardini / Brunello / Gigli / Montanari / Saccomanno; Udine University Hospital stroke unit + University of Udine). Simultaneous overnight Mindray vital-signs monitoring and Embletta level-3 PSG (Aug 2019–Jul 2020). PSG scored by a trained sleep physician (AASM rules); annotations temporally aligned to Mindray.","signals":["ppg","ecg","spo2","hr","resp","psg"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":30,"license":"CC0 1.0","gated":null,"wrist":false,"source_url":"https://doi.org/10.6084/m9.figshare.c.5630890","doi":"10.6084/m9.figshare.c.5630890","url":"/research/datasets/osasud"},{"slug":"paaws","name":"PAAWS R1","summary":"PAAWS R1 (Dec 2025, Northeastern / Intille lab; IMWUT 9(4) art. 204, doi:10.1145/3770639): ~4 h SimFL+Lab per person from 252 individuals with accelerometer/IMU at up to 21 locations (wrists top/bottom, waist, thighs, ankles) plus heart rate and metabolic data; ~7 days 24-h free-living from 20 adults (wrists, waist, thigh, ankle, phone); 29 PSG nights at home. Second-by-second video-annotated labels.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":252,"license":"In Copyright (rightsstatements.org InC) on Northeastern DRS; code repo GPL-3.0 — permission needed beyond copyright exceptions","gated":true,"wrist":null,"source_url":"https://www.paawsstudy.org/data.html","doi":null,"url":"/research/datasets/paaws"},{"slug":"pamap2","name":"PAMAP2","summary":"PAMAP2 (Physical Activity Monitoring for Aging People 2) released via UCI ML Repository (DOI 10.24432/C5NW2H). Protocol + optional sessions as .dat files (54 columns). Introductory papers: Reiss & Stricker ISWC 2012 / ABRA 2012.","signals":["accel","gyro","magnetometer","skin-temp","hr"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":9,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring","doi":"10.24432/C5NW2H","url":"/research/datasets/pamap2"},{"slug":"parkinsensedb","name":"ParkinSenseDB","summary":"ParkinSenseDB (Raggiunto / Belli / Paniccia et al.). Mendeley Data version 2, published 2026-09-12. DOI 10.17632/brpx8jwsh7.2.","signals":["emg","accel","gyro","magnetometer"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":53,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://data.mendeley.com/datasets/brpx8jwsh7/2","doi":"10.17632/brpx8jwsh7.2","url":"/research/datasets/parkinsensedb"},{"slug":"ppg-device-appel","name":"Photoplethysmography Device Data (Appel & Theart / Mendeley)","summary":"Stellenbosch Appel & Theart Mendeley release (Jan 2025). 8 files/participant (4 channels × 2 devices).","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":109,"license":"CC BY-NC 3.0","gated":null,"wrist":null,"source_url":"https://data.mendeley.com/datasets/bcb9329f6d/1","doi":"10.17632/bcb9329f6d.1","url":"/research/datasets/ppg-device-appel"},{"slug":"paee-utwente-cosmed-imu-ecg","name":"PM-EKF energy expenditure","summary":"Jobs: har.","signals":["accel","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":10,"license":"CC BY 4.0","gated":true,"wrist":false,"source_url":"https://zenodo.org/records/19388845","doi":"10.5281/zenodo.19388845","url":"/research/datasets/paee-utwente-cosmed-imu-ecg"},{"slug":"podcast-ring-ppg","name":"Podcast playback-speed physio","summary":"Scientific Data companion deposit (An / Pak / Désiron / Schneider / Donati; ETH Zurich / UZH / Fribourg). Version 1.0 zip playbackspeeddatasetv1.0.0.zip. English cohort includes workload/engagement/comprehension self-reports; raw EEG for 61/62.","signals":["ppg","eda","eeg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":62,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://zenodo.org/records/22541594","doi":"10.5281/zenodo.22541594","url":"/research/datasets/podcast-ring-ppg"},{"slug":"ppg-cognitive-fatigue-5gamers","name":"PPG Heart Beat for Cognitive Fatigue Prediction (5-Gamers)","summary":"Kaggle canaria/5-gamers. Long continuous recordings, sparse hourly annotations (SSS, sleep-2-peak RT, caffeine/food). GitHub: canaria-technologies/kaggle-5-gamers-input.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":5,"license":null,"gated":null,"wrist":false,"source_url":"https://www.kaggle.com/datasets/canaria/5-gamers","doi":null,"url":"/research/datasets/ppg-cognitive-fatigue-5gamers"},{"slug":"ppg-bgl-kermani","name":"PPG Signals for Blood Glucose Measurement (Kermani / Mendeley)","summary":"Mendeley dataset 37pm7jk7jn (v3 cited). Fingertip green PPG for BGL.","signals":["ppg"],"ppg_sites":["finger"],"ppg_wavelengths_nm":"green 550 nm","bp_reference":[],"subjects":23,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://data.mendeley.com/datasets/37pm7jk7jn/3","doi":"10.17632/37pm7jk7jn.3","url":"/research/datasets/ppg-bgl-kermani"},{"slug":"ppg-bgl-smartwatch-bhatia","name":"PPG-BGL Smartwatch (Bhatia & Ekbote 2026 / Zenodo 20577959)","summary":"Bhatia & Ekbote exploratory blood-glucose smartwatch study. Not the same record as ppg-cgm-glucose-zenodo (20691830).","signals":["ppg","cgm"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":1,"license":null,"gated":null,"wrist":true,"source_url":"https://zenodo.org/records/20577959","doi":"10.5281/zenodo.20577959","url":"/research/datasets/ppg-bgl-smartwatch-bhatia"},{"slug":"ppgbp-guilin","name":"PPG-BP Database (Guilin / Liang 2018)","summary":"PPG-BP Database (Liang et al. lineage; Guilin). Fingertip reflective PPG with one cuff BP reading per subject and hypertension-stage labels.","signals":["ppg"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["cuff"],"subjects":219,"license":"CC0 1.0","gated":null,"wrist":false,"source_url":"https://figshare.com/articles/dataset/PPG-BP_Database/5459299","doi":null,"url":"/research/datasets/ppgbp-guilin"},{"slug":"ppg-dalia","name":"PPG-DaLiA","summary":"PPG-DaLiA (Reiss / Indlekofer / Schmidt / Van Laerhoven lineage; Uni-Siegen). Daily-life activities with wrist PPG+accel and chest physiological reference. USI page: https://ubi29.informatik.uni-siegen.de/usi/datappgdalia.html.","signals":["ppg","ecg","accel","skin-temp","resp"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":15,"license":"Scientific, non-commercial use with credit to the owners","gated":null,"wrist":true,"source_url":"https://uni-siegen.sciebo.de/s/pfHzlTepXkiJ4jP","doi":null,"url":"/research/datasets/ppg-dalia"},{"slug":"ppg-signal-dataset-usc-cali","name":"PPG-Signal-Dataset (USC Cali)","summary":null,"signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":56,"license":null,"gated":null,"wrist":null,"source_url":"https://github.com/sanvsquezsz/PPG-based-BP-assessment","doi":"10.1007/s13246-025-01593-3","url":"/research/datasets/ppg-signal-dataset-usc-cali"},{"slug":"ppg-sport","name":"PPG-Sport","summary":"PPG-Sport (Hu / Pham / … / Ma), Proc. ACM IMWUT 2026, DOI 10.1145/3810216. First large public wrist-PPG sports set targeting non-periodic, high-intensity, bilaterally asymmetric motion.","signals":["ppg","ecg","accel","gyro"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":30,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://github.com/LaserHu/PPG-Sport","doi":"10.1145/3810216","url":"/research/datasets/ppg-sport"},{"slug":"ppgarrhythmia","name":"PPGArrhythmia (Liu 2022)","summary":"PulseLM cites full 118,217 segments / 228 patients. Only validtestDataset/ (~91 .mat files) is on GitHub. Full train set requires author access.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":91,"license":null,"gated":null,"wrist":false,"source_url":"https://github.com/zdzdliu/PPGArrhythmiaDetection","doi":"10.1161/JAHA.121.023555","url":"/research/datasets/ppgarrhythmia"},{"slug":"ptb-xl","name":"PTB-XL","summary":"21,799 10-s 12-lead ECGs from 18,869 patients (Schiller devices, 1989–1996), annotated with 71 SCP-ECG statements (diagnostic, form, rhythm — rhythm statements include AF), with recommended 10-fold patient-wise splits (folds 9–10 human-validated). v1.0.3, 3.0 GB, open access. Source: PhysioNet page (fetched 2026-10-12).","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":18869,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/ptb-xl/1.0.3/","doi":"10.13026/kfzx-aw45","url":"/research/datasets/ptb-xl"},{"slug":"pulse-transit-time-ppg","name":"Pulse Transit Time PPG","summary":"Pulse Transit Time PPG Dataset v1.1.0 (PhysioNet). High-resolution synchronized multi-sensor finger PPG under three activities.","signals":["ppg","ecg","accel","gyro"],"ppg_sites":["finger"],"ppg_wavelengths_nm":"ir 880 / red 660 / green 537 nm","bp_reference":["cuff"],"subjects":22,"license":"ODbL 1.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/pulse-transit-time-ppg/1.1.0/","doi":"10.13026/jpan-6n92","url":"/research/datasets/pulse-transit-time-ppg"},{"slug":"pulse-wave-database","name":"Pulse Wave Database (PWDB)","summary":null,"signals":["ppg","abp"],"ppg_sites":["finger","wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":4374,"license":"ODC-PDDL","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/3275625","doi":"10.5281/zenodo.3275625","url":"/research/datasets/pulse-wave-database"},{"slug":"pulsedb","name":"PulseDB v2.0","summary":"Wang et al., PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods, Frontiers in Digital Health (2023), doi:10.3389/fdgth.2022.1090854. Repo: https://github.com/pulselabteam/PulseDB (@db0824f, v20 on main). The v2.0 release holds 5,361 subject files of 10 s ECG/PPG/ABP segments (README).","signals":["ppg","ecg","abp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line","cuff"],"subjects":5361,"license":"Split by folder: PulseDB_MIMIC files under ODbL 1.0 (contents under DbCL 1.0); PulseDB_Vital files and the subset/Info files under CC BY-NC-SA 4.0","gated":null,"wrist":false,"source_url":"https://github.com/pulselabteam/PulseDB","doi":"10.3389/fdgth.2022.1090854","url":"/research/datasets/pulsedb"},{"slug":"pulsewatch","name":"Pulsewatch","summary":"Pulsewatch is a Synapse-hosted clinical/ambulatory AF monitoring dataset and companion code release (UConn / PulsewatchRelease). Companion ICASSP 2025 training/eval code documents 30 s PPG/HR/ACC products and AF adjudication labels.","signals":["ppg","ecg","accel"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":106,"license":null,"gated":true,"wrist":true,"source_url":"https://www.synapse.org/Synapse:syn23565056","doi":null,"url":"/research/datasets/pulsewatch"},{"slug":"ppg-bp-annot-zenodo","name":"pyPPG benchmarking","summary":"Zenodo record 10523285 (publication date 2024-01-17; pinned by record id + file md5, no metadata.version string). Title: pyPPG benchmarking. Acquired archive: PPG-BPannot.zip only (results.zip deliberately not downloaded).","signals":[],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":219,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/10523285","doi":"10.5281/zenodo.10523285","url":"/research/datasets/ppg-bp-annot-zenodo"},{"slug":"qol-stress","name":"QoL_Stress","summary":"QoLStress is a multimodal physiological + self-report stress dataset for stress detection research. Zenodo record 20757481 (created 2026-06-22), license CC BY 4.0. Collections: ecg, eda, Fitbit HR streams, demographics, PSS-14, STAI-S.","signals":["ecg","eda","fitbit-hr"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":66,"license":"CC BY 4.0","gated":null,"wrist":"mixed","source_url":"https://zenodo.org/records/20757481","doi":"10.5281/zenodo.20757481","url":"/research/datasets/qol-stress"},{"slug":"radboud-ptt-ppg-bp","name":"Radboud PTT PPG+BP (Mol 2020)","summary":"Official title: Pulse transit time as a proxy for vasoconstriction in younger and older adults (version 1). Creators: Arjen Mol, Carel G.M. Meskers, Sjoerd P.","signals":["ppg","ecg","bp-finapres","tpr","nirs","spo2","resp"],"ppg_sites":["finger","wrist"],"ppg_wavelengths_nm":null,"bp_reference":["finapres"],"subjects":86,"license":"RU-DI-HD-1.0: Radboud Data Repository data use agreement for identifiable human data","gated":null,"wrist":"mixed","source_url":"https://data.ru.nl/collections/di/dcn/DSC_626810_0007_173","doi":"10.34973/te70-x603","url":"/research/datasets/radboud-ptt-ppg-bp"},{"slug":"reallifehar","name":"Real-Life HAR (UDC)","summary":"Smartphone accelerometer, gyroscope, magnetometer and GPS collected in real life (Universidade da Coruña). Phone-only. Awesome list: 19 users.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":19,"license":null,"gated":null,"wrist":null,"source_url":"https://lbd.udc.es/research/real-life-HAR-dataset/","doi":null,"url":"/research/datasets/reallifehar"},{"slug":"realworld-ppg-siam","name":"Real-World PPG Dataset (Siam et al. / Mendeley)","summary":"Mendeley yynb8t9x3d v3. Pre-split short segments; age 10–75.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":35,"license":"CERN OHL","gated":null,"wrist":null,"source_url":"https://data.mendeley.com/datasets/yynb8t9x3d/3","doi":"10.17632/yynb8t9x3d.3","url":"/research/datasets/realworld-ppg-siam"},{"slug":"realdisp","name":"REALDISP","summary":"17 subjects, warm-up, cool-down and fitness exercises; acceleration, rate of turn, magnetic field and quaternions; ideal-placement, self-placement and induced-displacement conditions. UCI, CC BY 4.0.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":17,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://archive.ics.uci.edu/dataset/305/realdisp+activity+recognition+dataset","doi":null,"url":"/research/datasets/realdisp"},{"slug":"realworld-har","name":"RealWorld HAR","summary":"Introduced with On-body localization of wearable devices… (IEEE PerCom 2016; Sztyler & Stuckenschmidt, University of Mannheim DWS). Subjects performed climbing stairs down/up, jumping, lying, standing, sitting, running/jogging, and walking while seven on-body devices recorded sensors concurrently. Official landing: Mannheim DWS RealWorld dataset page.","signals":["accel","gyro","magnetometer","audio","gps","light"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":15,"license":null,"gated":null,"wrist":"mixed","source_url":"https://www.uni-mannheim.de/dws/research/projects/activity-recognition/dataset/dataset-realworld/","doi":null,"url":"/research/datasets/realworld-har"},{"slug":"recgym","name":"RecGym","summary":"10 volunteers, sensing unit (acc, gyro, body-capacitance) at wrist, pocket and calf, 20 Hz; 11 workouts + null; each participant 5 sessions on 5 days (~1 h each), 50 sessions total.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":10,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://zhaxidele.github.io/RecGym/","doi":null,"url":"/research/datasets/recgym"},{"slug":"brain-wearable-monitoring","name":"Regulation of Brain Cognitive States through Auditory, Gustatory, and Olfactory Stimulation with Wearable Monitoring","summary":null,"signals":["ppg","accel","eda","eeg"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"ODC-By 1.0","gated":null,"wrist":true,"source_url":"https://physionet.org/content/brain-wearable-monitoring/1.0.0/","doi":"10.13026/jr6f-wc85","url":"/research/datasets/brain-wearable-monitoring"},{"slug":"respiratory-oximetry-apnoea","name":"Respiratory and Pulse Oximetry Waveforms from Healthy Adults During Simulated Apnoea Events","summary":null,"signals":["ppg","resp"],"ppg_sites":["neck"],"ppg_wavelengths_nm":"red 660 / ir 940 nm","bp_reference":[],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/respiratory-oximetry-apnoea/1.0.0/","doi":"10.13026/s45r-k263","url":"/research/datasets/respiratory-oximetry-apnoea"},{"slug":"rest-asmr-ppg","name":"REST-ASMR PPG Dataset (Zenodo)","summary":"Finger distal-phalanx PPG (raw + 0.5–35 Hz bandpass) ~12 min sessions. Behavioural labels time-aligned.","signals":["ppg"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":34,"license":null,"gated":null,"wrist":false,"source_url":"https://zenodo.org/records/18881334","doi":"10.5281/zenodo.18881334","url":"/research/datasets/rest-asmr-ppg"},{"slug":"samsung-afib-pvc-patch-study","name":"Samsung AFib/PVC study","summary":"Samsung study pairing watch PPG with an ambulatory ECG patch. xMAE describes it as a lab setting; MMR's Table 4 labels it \"Naturalistic (field)\". The setting is therefore reported inconsistently.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":139,"license":"Private (Samsung). xMAE: unable to release the evaluated datasets.","gated":true,"wrist":true,"source_url":"https://doi.org/10.48550/arXiv.2605.00973","doi":null,"url":"/research/datasets/samsung-afib-pvc-patch-study"},{"slug":"samsung-bp-free-living","name":"Samsung BP free-living study","summary":"Free-living Samsung study pairing watch PPG with cuff BP readings. HiMAE calls it \"My BP Lab (Clinical Trials ID 19-27169)\", free-world setting.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":9427,"license":"Private (Samsung). xMAE: unable to release the evaluated datasets.","gated":true,"wrist":true,"source_url":"https://doi.org/10.48550/arXiv.2605.00973","doi":null,"url":"/research/datasets/samsung-bp-free-living"},{"slug":"samsung-bp-lab-cnap","name":"Samsung BP lab study","summary":"Samsung lab study pairing resting wrist PPG with CNAP beat-to-beat finger BP. HiMAE calls it the \"My Heart Lab Study (NCT04314947)\". Conflict: ClinicalTrials.gov lists NCT04314947's org study ID as \"MyHeartLab Free Living Study\" (record details withheld because the device isn't FDA-cleared), so the NCT mapping to this lab set is unconfirmed.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":135,"license":"Private (Samsung). xMAE: unable to release the evaluated datasets.","gated":true,"wrist":true,"source_url":"https://doi.org/10.48550/arXiv.2605.00973","doi":null,"url":"/research/datasets/samsung-bp-lab-cnap"},{"slug":"samsung-tulane-lab-tests","name":"Samsung Galaxy Watch PPG + clinical lab panel (Tulane University study 20242033; private)","summary":"Watch PPG paired with clinical lab values. Used as \"lab tests\" regression/classification targets.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":45,"license":"Private (Samsung / Tulane). xMAE: unable to release the evaluated datasets.","gated":true,"wrist":true,"source_url":"https://doi.org/10.48550/arXiv.2510.25785","doi":null,"url":"/research/datasets/samsung-tulane-lab-tests"},{"slug":"samsung-galaxy-watch-pretraining-corpus","name":"Samsung Galaxy Watch PPG pretraining corpus (~32K users, ~17M 10-s segments; private)","summary":"Free-living wrist PPG pooled from Samsung research studies.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":32000,"license":"Private.","gated":true,"wrist":true,"source_url":"https://doi.org/10.48550/arXiv.2601.12215","doi":null,"url":"/research/datasets/samsung-galaxy-watch-pretraining-corpus"},{"slug":"samsung-mx-ectopic","name":"Samsung MX free-living ectopic-beat set","summary":"Free-living watch PPG with ectopic-beat labels. The labeling source is not detailed in xMAE.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":2613,"license":"Private (Samsung). xMAE: unable to release the evaluated datasets.","gated":true,"wrist":true,"source_url":"https://doi.org/10.48550/arXiv.2605.00973","doi":null,"url":"/research/datasets/samsung-mx-ectopic"},{"slug":"scientisst-move-biosignals","name":"ScientISST MOVE: Annotated Wearable Multimodal Biosignals recorded during Everyday Life Activities in Naturalistic Environments","summary":null,"signals":["ppg","ecg","accel","eda"],"ppg_sites":["chest","finger","wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":17,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://physionet.org/content/scientisst-move-biosignals/1.0.1/","doi":"10.13026/hyxq-r919","url":"/research/datasets/scientisst-move-biosignals"},{"slug":"sdb","name":"SDB","summary":"Processed shards under processed/sdb/. Not re-downloaded.","signals":["ppg","spo2"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":146,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://doi.org/10.6084/m9.figshare.1209662","doi":"10.6084/m9.figshare.1209662","url":"/research/datasets/sdb"},{"slug":"sensors-aguirre","name":"Sensors Aguirre 2021","summary":"Processed MIMIC-III matched ABP+PPG segments (Zenodo 4598938).","signals":["ppg","abp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":1195,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/4598938","doi":"10.5281/zenodo.4598938","url":"/research/datasets/sensors-aguirre"},{"slug":"senssmarttech","name":"SensSmartTech","summary":"SensSmartTech (Lazović et al.; PhysioNet v1.0.0, published 2024-12-19). First multisensory base systematically following heart relaxation dynamics across a wide HR range. CSV = original rates; WFDB = resampled to 1 kHz common axis.","signals":["ppg","ecg","accel","pcg"],"ppg_sites":["neck"],"ppg_wavelengths_nm":"red 660 / ir 800 nm","bp_reference":["cuff"],"subjects":32,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/senssmarttech/1.0.0/","doi":"10.13026/fy9p-n277","url":"/research/datasets/senssmarttech"},{"slug":"shdb-af","name":"SHDB-AF","summary":"SHDB-AF (Tsutsui / Biton Brimer / Behar et al.; Sci Data 2025 10.1038/s41597-025-04777-4). Built to stress-test AF event detection under distribution shift. Collected May 2019–May 2023 (Saitama).","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":122,"license":"ODC-By 1.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/shdb-af/1.0.1/","doi":"10.13026/n6yq-fq90","url":"/research/datasets/shdb-af"},{"slug":"sicbaldi-wrist-bp-stratification","name":"Sicbaldi wrist PPG BP stratification","summary":"Companion repo to Sicbaldi et al., CinC 2024 (doi 10.22489/CinC.2024.438): Empatica E4 wrist PPG (64 Hz) + ACC (32 Hz) in-lab quiet sitting and 24 h free-living, split into wake and sleep, against Mobil-O-Graph 24 h ABPM; 23 pyPPG morphology features, low vs high BP groups and hour-by-hour trends. Data not hosted in repo → request from authors (gated). 24 h is one day, not multi-day; repeated ABPM readings within that day make it a BP-trend (diurnal) candidate.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":39,"license":null,"gated":null,"wrist":null,"source_url":"https://github.com/marcellosicbaldi/BloodPressure_stratification_PPG","doi":null,"url":"/research/datasets/sicbaldi-wrist-bp-stratification"},{"slug":"simultaneous-measurements","name":"Simultaneous physiological measurements with five devices at different cognitive and physical loads","summary":null,"signals":["ppg","ecg","accel","resp","spo2","hr"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":["cuff"],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/simultaneous-measurements/1.0.2/","doi":"10.13026/wce5-fj54","url":"/research/datasets/simultaneous-measurements"},{"slug":"skoda-assembly","name":"Skoda mini checkpoint","summary":"Car-assembly checkpoint gestures with accelerometers on both arms (2008). Source page not re-checked; all facts from Awesome-IMU-Sensing (1 subject).","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":null,"gated":null,"wrist":null,"source_url":"http://har-dataset.org/","doi":null,"url":"/research/datasets/skoda-assembly"},{"slug":"ppg-cgm-glucose-zenodo","name":"Smartwatch PPG paired with CGM glucose (Zenodo)","summary":"Exploratory blood-glucose study with smartwatch PPG waveforms paired to CGM values.","signals":["ppg","cgm"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":5,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/20691830","doi":"10.5281/zenodo.20691830","url":"/research/datasets/ppg-cgm-glucose-zenodo"},{"slug":"sonata","name":"SONATA","summary":"SONATA provides multimodal physiological signals acquired during emotionally-labeled music listening for affective computing / emotion recognition research. Hosted on Zenodo record 22641079 (DOI 10.5281/zenodo.22641079), license CC BY 4.0. Archive: SONATA.zip (~2.4 GB).","signals":["ppg","ecg","skin-temp","eda","resp"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":50,"license":"CC BY 4.0","gated":null,"wrist":"mixed","source_url":"https://zenodo.org/records/22641079","doi":"10.5281/zenodo.22641079","url":"/research/datasets/sonata"},{"slug":"srf-cognitive-adaptation-eeg-eda-bvp","name":"SRF cognitive adaptation","summary":"Three tasks (pour and drink, simulated driving, shape sorting) under three SRF conditions. Companion papers: TNSRE 2025 (10.1109/TNSRE.2025.3574133), IJRR 2026 (10.1177/02783649261436484), TNSRE 2026 (10.1109/TNSRE.2026.3658402).","signals":["eeg","eda","ppg","resp"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":30,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://doi.org/10.6084/m9.figshare.32948735.v2","doi":"10.6084/m9.figshare.32948735.v2","url":"/research/datasets/srf-cognitive-adaptation-eeg-eda-bvp"},{"slug":"stanford-ppg","name":"Stanford wrist PPG AF (DeepBeat)","summary":"Torres-Soto J, Ashley EA. Multi-task deep learning for cardiac rhythm detection in wearable devices. npj Digital Medicine 3, 116 (2020).","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"Non-commercial data use agreement (data); GPL-3.0 (code)","gated":null,"wrist":true,"source_url":"https://www.synapse.org/Synapse:syn21985690","doi":"10.1038/s41746-020-00320-4","url":"/research/datasets/stanford-ppg"},{"slug":"stillness-2025-deichman","name":"Stillness 2025 (Deichman)","summary":"Stillness 2025 Deichman (Jensenius / Serdar / Guo / Bjerkestrand / Johnson / Rao; University of Oslo / RITMO). Zenodo concept 21840411; latest version 1.0.4 record 21964815 (2026-08-16). Part of Oslo Standstill Database family.","signals":["ecg","accel","resp","skin-temp","hr","rr-interval"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":16,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/21964815","doi":"10.5281/zenodo.21964815","url":"/research/datasets/stillness-2025-deichman"},{"slug":"stress-predict","name":"Stress-Predict Dataset (E4, MIT)","summary":"Pilot stress-monitoring dataset from Sensors 2022 (10.3390/s22218135). Open GitHub release by Iqbal et al. Three stress-inducing tasks + rest blocks.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":35,"license":"MIT","gated":null,"wrist":true,"source_url":"https://github.com/italha-d/Stress-Predict-Dataset","doi":"10.3390/s22218135","url":"/research/datasets/stress-predict"},{"slug":"stuba-ppg-bgl","name":"STU-BA PPG Blood Glucose Dataset (Zenodo)","summary":"Multi-wavelength thumb PPG for blood-glucose estimation.","signals":["ppg","accel"],"ppg_sites":["finger"],"ppg_wavelengths_nm":"green 530 / red 655 / ir 940 nm","bp_reference":[],"subjects":24,"license":"CC0 1.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/21978226","doi":"10.5281/zenodo.21978226","url":"/research/datasets/stuba-ppg-bgl"},{"slug":"tau-ring-dataset","name":"tau-ring-dataset","summary":"Source dataset for τ-Ring: A Dataset and Toolkit for Multiparameter Cardiovascular Physiology Sensing on Rings (Tang et al., Tsinghua, 2025, arXiv:2505.04172) and reused by the CITRUS thesis for ring-side motion experiments. Two custom ring prototypes (reflective / transmissive optical paths), IR+red PPG at 100 Hz, 3-axis accel, 34 subjects, ground truth from oximeter/resp-band/BP-cuff/ECG-patch depending on sub-cohort.","signals":["accel","ppg-ir","ppg-red"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":null,"gated":null,"wrist":null,"source_url":"https://github.com/thuhci/RingTool","doi":null,"url":"/research/datasets/tau-ring-dataset"},{"slug":"texatrode-textile-holter-ecg-sqa","name":"Texatrode long-term ECG","summary":"Data for \"Introduction to electrocardiogram signal quality assessment and estimated accuracy for textile electrodes\" (Sci Rep 2025, 10.1038/s41598-025-25365-x). Its SQA sorts segments into 4 quality classes by the estimated accuracy of RR intervals.","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":13,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://zenodo.org/records/22874770","doi":"10.5281/zenodo.22874770","url":"/research/datasets/texatrode-textile-holter-ecg-sqa"},{"slug":"three-wearable","name":"Three-wearable","summary":"Physiological signals from wearable devices in two scenarios (Lucas Pérez / Martínez-Acha / Serrano-Mamolar / Arnaiz-González / Katsigiannis): (1) simultaneous Empatica E4 + Shimmer3 GSR+ + EmotiBit; (2) placement-dependent Shimmer3 + EmotiBit (volar/dorsal wrist, finger). Pipeline: https://github.com/gadea-lucas/wearables-signal-comparison.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":"mixed","source_url":"https://zenodo.org/records/19218368","doi":"10.5281/zenodo.19218368","url":"/research/datasets/three-wearable"},{"slug":"tnda-har","name":"TNDA-HAR","summary":"IEEE DataPort open access (doi:10.21227/4epb-pg26). Instructions: 23 subjects, 8 activities (sit, stand, lay, walk, run, cycle, stairs up/down), ~2 min each; acc/gyro/mag at wrist, ankle and back. CSV, 353.7 MB zip.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":23,"license":null,"gated":null,"wrist":null,"source_url":"https://ieee-dataport.org/open-access/tnda-har-0","doi":null,"url":"/research/datasets/tnda-har"},{"slug":"tolife-gw5","name":"ToLife","summary":"ToLife provides simultaneous consumer-watch wrist PPG, contact finger PPG, and ECG. It is especially useful for learning timing/rate relationships and testing cross-site representations without assuming that fingertip contour or amplitude is preserved at the wrist.","signals":["ppg","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":20,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/10717903","doi":"10.5281/zenodo.10717903","url":"/research/datasets/tolife-gw5"},{"slug":"tolife-csa-health-indices","name":"TOLIFE Clinical Study A","summary":"Zenodo v2.0 (published 2026-10-09; Tognetti, Laurino et al.; EU HORIZON TOLIFE, GA 101057103). 86 COPD patients across Germany, Spain and Italy followed 12 months (enrolled May 2024 to Sep 2025; monitoring May 2024 to Apr 2026) with the TOLIFE sensor kit (commercial devices incl. smartphone and smartwatch), with periodic clinical exams as reference.","signals":["derived-health-indices"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":86,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://zenodo.org/records/23263785","doi":"10.5281/zenodo.23263785","url":"/research/datasets/tolife-csa-health-indices"},{"slug":"triple-wavelength-hgb-abuzairi","name":"Triple-Wavelength PPG for Hemoglobin (Abuzairi / Mendeley)","summary":"Mendeley 8xbbck45tt v2. Raw / filtered / peak folders.","signals":["ppg"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":52,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://data.mendeley.com/datasets/8xbbck45tt/2","doi":"10.17632/8xbbck45tt.2","url":"/research/datasets/triple-wavelength-hgb-abuzairi"},{"slug":"tutunji-prolonged-stress-e4","name":"Tutunji prolonged stress","summary":"Collection Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study (version 1), Radboud University / Donders Centre for Cognitive Neuroimaging, collection di.dccn.DSC3013068.02912. Authors: Tutunji R., Kogias N., Kapteijns B., Krentz M., Krause F., Vassena E., Hermans E.J. Companion paper DOI 10.2196/39995 (JMIR).","signals":["ppg","accel","eda","skin-temp","hr","ibi"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":83,"license":"RU-HD-SU-1.0: Radboud Data Repository data use agreement for identifiable human data, scientific use","gated":true,"wrist":true,"source_url":"https://data.ru.nl/collections/di/dccn/DSC_3013068.02_912","doi":"10.34973/j0ym-mb28","url":"/research/datasets/tutunji-prolonged-stress-e4"},{"slug":"two-wearables-placements","name":"Two-wearables placements","summary":"Placement-focused Scenario 2 release of a multi-wearable comparison study (Empatica E4 simultaneous Scenario 1 not included in this version). Zenodo 22655618, DOI 10.5281/zenodo.22655618, license CC BY 4.0. Companion processing: https://github.com/gadea-lucas/wearables-signal-comparison","signals":["ppg","accel","gyro","magnetometer","skin-temp","eda"],"ppg_sites":["finger","wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":10,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/22655618","doi":"10.5281/zenodo.22655618","url":"/research/datasets/two-wearables-placements"},{"slug":"ubfc-phys","name":"UBFC-Phys","summary":"UBFC-Phys (Université Bourgogne Franche-Comté / Meziati Sabour et al.). Companion paper in IEEE Transactions on Affective Computing 2021.","signals":["eda","ppg-bvp","video-face"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":56,"license":null,"gated":null,"wrist":true,"source_url":"https://sites.google.com/view/ybenezeth/ubfc-phys","doi":null,"url":"/research/datasets/ubfc-phys"},{"slug":"uci-cuffless-bp","name":"UCI Cuff-Less Blood Pressure Estimation (UCI 340; Kachuee 2015)","summary":"Kachuee, Kiani, Mohammadzade, Shabany. Introductory paper: \"Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time\" (ISCAS 2015). The UCI page says the raw ECG, PPG and ABP were \"originally collected from the physionet.org\" and then preprocessed and validated.","signals":["ppg","abp","ecg"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":null,"license":"CC BY 4.0 (UCI listing); upstream MIMIC-II provenance review pending","gated":null,"wrist":false,"source_url":"https://archive.ics.uci.edu/dataset/340/cuff+less+blood+pressure+estimation","doi":"10.24432/C5B602","url":"/research/datasets/uci-cuffless-bp"},{"slug":"ucsf-icu-waveforms-private","name":"UCSF ICU bedside-monitor waveforms (private; GPT-PPG / SiamQuality / SQUWA pretraining)","summary":"Continuous bedside-monitor waveforms plus arrhythmia alarms and EHR from UCSF Medical Center ICUs, collected March 2013 – December 2018. Used under UCSF IRB 14-13262 (waiver of consent). The Emory authors access it through a UCSF–Emory data-use agreement.","signals":["ppg","ecg","resp"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":24100,"license":"Private. UCSF IRB 14-13262 (waiver of consent); used by Emory under a UCSF–Emory DUA. No public release.","gated":true,"wrist":false,"source_url":"https://doi.org/10.48550/arXiv.2503.08015","doi":null,"url":"/research/datasets/ucsf-icu-waveforms-private"},{"slug":"uk-biobank-accelerometer","name":"UK Biobank wrist accelerometer (Axivity AX3 / field 90001)","summary":"UK Biobank category 1008 / field 90001 “Acceleration data – cwa format”. Main collection ~2013–2016; seasonal repeats for a subset. Official field page: https://biobank.ctsu.ox.ac.uk/ukb/field.cgi?id=90001.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":103568,"license":"UK Biobank Material Transfer Agreement (approved researchers only)","gated":true,"wrist":true,"source_url":"https://biobank.ctsu.ox.ac.uk/ukb/field.cgi?id=90001","doi":null,"url":"/research/datasets/uk-biobank-accelerometer"},{"slug":"umafall","name":"UMAFall","summary":"Mobility traces from 19 experimental subjects emulating predetermined ADLs and falls; smartphone plus wearable motion sensors; video clips of movements included. Must cite Casilari et al. 2017 and Santoyo-Ramón et al.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":19,"license":null,"gated":null,"wrist":null,"source_url":"https://figshare.com/articles/dataset/UMA_ADL_FALL_Dataset_zip/4214283","doi":null,"url":"/research/datasets/umafall"},{"slug":"universe-mwl-stress","name":"UNIVERSE (mental workload + stress, ~315h E4)","summary":"UNobtrusIVE measuRement of mental workload and stress in uncontrolled environments (~315 h total). Zenodo DOI 10.5281/zenodo.10371068. Scientific Data 2024.","signals":["ppg","accel","skin-temp","eda","eeg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":24,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/10371068","doi":"10.5281/zenodo.10371068","url":"/research/datasets/universe-mwl-stress"},{"slug":"uqvitalsigns","name":"UQVitalSigns","summary":"32 surgical cases; multi-parameter OR monitoring including pulse-ox pleth. CC BY-NC 3.0 → stored under raw/restricted/. Not for pretrain allowlist.","signals":["ppg","ecg","resp","spo2","co2","bp"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":32,"license":"CC BY-NC 3.0","gated":null,"wrist":false,"source_url":"https://outbox.eait.uq.edu.au/uqdliu3/uqvitalsignsdataset/","doi":"10.1213/ANE.0b013e318241f7c0","url":"/research/datasets/uqvitalsigns"},{"slug":"usc-had","name":"USC-HAD","summary":"USC-SIPI Human Activity Dataset (UbiComp SAGAware 2012). Page states no licence. MotionNode IMU at hip; N=14 per Awesome-IMU-Sensing (paper not re-read).","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":14,"license":null,"gated":null,"wrist":null,"source_url":"https://sipi.usc.edu/had/","doi":null,"url":"/research/datasets/usc-had"},{"slug":"utsa-ppg","name":"UTSA-PPG","summary":"12 subjects × 3 scenarios (sit/sleep/office), ≥30 min each; wrist green + finger IR/red + accel + ECG ref.","signals":["ppg","ecg","accel"],"ppg_sites":["finger","wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":12,"license":"MIT","gated":null,"wrist":true,"source_url":"https://github.com/utsanpb2-234/UTSA-PPG","doi":"10.1109/ICHI64645.2025.00042","url":"/research/datasets/utsa-ppg"},{"slug":"vitaldb","name":"VitalDB","summary":null,"signals":["ppg","ecg","resp","abp","co2"],"ppg_sites":["finger"],"ppg_wavelengths_nm":null,"bp_reference":["arterial-line"],"subjects":6388,"license":"CC BY 4.0 (PhysioNet release)","gated":null,"wrist":false,"source_url":"https://physionet.org/content/vitaldb/1.0.0/","doi":"10.13026/czw8-9p62","url":"/research/datasets/vitaldb"},{"slug":"vitaldb-arrhythmia","name":"VitalDB Arrhythmia","summary":"VitalDB Arrhythmia Database (Eun / Shim / Lee / Lim / Lim / Lee / Lee). PhysioNet v1.0.0 published 2026-02-26. DOI 10.13026/axd6-wm13.","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":482,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://physionet.org/content/vitaldb-arrhythmia/1.0.0/","doi":"10.13026/axd6-wm13","url":"/research/datasets/vitaldb-arrhythmia"},{"slug":"vitalvideos","name":"VitalVideos","summary":"P.-J. Toye (VitalVideos, Belgium). Europe-1 is described in arXiv 2306.11891: 850 participants, two 30-s videos each, synchronized PPG, one BP reading.","signals":["ppg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":850,"license":"Custom licence: academic DTA (signed, institutional) or commercial on request (pj@vitalvideos.org)","gated":true,"wrist":false,"source_url":"https://vitalvideos.org/","doi":null,"url":"/research/datasets/vitalvideos"},{"slug":"w-har","name":"w-HAR","summary":"Source code and data sets for online HAR (Bhat et al.); ankle IMU plus knee stretch sensor. Repo licence GPL-3.0; applicability to training data/weights unclear. Awesome list: 22 users, ~3 h.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"GPL-3.0 (repository; covers code and data sets in repo)","gated":null,"wrist":null,"source_url":"https://github.com/gmbhat/human-activity-recognition","doi":null,"url":"/research/datasets/w-har"},{"slug":"walker-posture-e4","name":"Walker posture E4","summary":"“Evaluating the validity of walker sensors for posture monitoring” (Fernández-Carmona / Urdiales / Gómez-de-Gabriel; Universidad de Málaga / TaISLab). ROS2 bags converted to per-trial CSV zips (202404test.zip, 202406test.zip). Code: https://github.com/TaISLab/WalKit.","signals":["ppg","accel","skin-temp","eda"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/22734705","doi":"10.5281/zenodo.22734705","url":"/research/datasets/walker-posture-e4"},{"slug":"warehouse-moc-sensor","name":"Warehouse MoC+Sensor","summary":"Rokoko full-body MoCap suit time-aligned with a physical right-wrist Garmin Venu accelerometer; warehouse/logistics actions manually segmented with synchronized video and beat instruction. Repo licence MIT; data-specific terms not separately stated — verdict left unknown pending confirmation. Awesome list: 9 subjects, 4.4 h.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":9,"license":"MIT","gated":null,"wrist":null,"source_url":"https://github.com/xieyulai/warehouse-moc-sensor","doi":null,"url":"/research/datasets/warehouse-moc-sensor"},{"slug":"warm-vr","name":"WARM-VR","summary":"Alghoul, Mohd (Faisal), Laamarti, Al Osman, El Saddik (University of Ottawa). ACM TOMM 2026, DOI 10.1145/3836767; arXiv 2605.00184. Dataset DOI 10.21227/rwrm-n531.","signals":["ppg","eda","skin-temp","accel","ecg"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":28,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://doi.org/10.21227/rwrm-n531","doi":"10.21227/rwrm-n531","url":"/research/datasets/warm-vr"},{"slug":"watchmyheart","name":"WatchMyHeart","summary":"WatchMyHeart (Alkhodari / Khandoker, Khalifa University). Zenodo 20729337 (concept 20729336), published 2026-06-22; files modified 2026-08-11. Release: Rawsignals.mat, Metadata.xlsx, Qualitysummaryperparticipant.xlsx.","signals":["ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":93,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://zenodo.org/records/20729337","doi":"10.5281/zenodo.20729337","url":"/research/datasets/watchmyheart"},{"slug":"wear-sports","name":"WEAR","summary":"22 participants, 18 workout activities, synchronized acceleration and egocentric video at 11 outdoor locations (IMWUT 8(4) art. 175, 2024). Licence CC BY-NC-SA 4.0 — non-commercial.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":22,"license":"CC BY-NC-SA 4.0","gated":null,"wrist":null,"source_url":"https://mariusbock.github.io/wear/","doi":null,"url":"/research/datasets/wear-sports"},{"slug":"wearanize-plus-oa","name":"Wearanize+ OA","summary":"Wearanize+ OA is the open-access release of Wearanize+ (Donders / Radboud; Dresler / Zerr / Sikder). Each participant slept one night with three wearables plus PSG. PSG stages include two expert manual scorers plus U-Sleep v2.0 autoscores.","signals":["ppg","accel","skin-temp","eda","eeg","eog","psg"],"ppg_sites":["forehead","wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":130,"license":"CC BY 4.0 (LICENSE.txt); ABOUT.txt limits use to scientific research","gated":null,"wrist":true,"source_url":"https://data.ru.nl/collections/di/dcmn/DSC_wrnzpoa_t0000925a_195","doi":"10.34973/xrmf-5726","url":"/research/datasets/wearanize-plus-oa"},{"slug":"wenxinwuyang-handheld-ecg-ppg","name":"WenXinWuYang","summary":"Synchronized PPG and single-lead ECG from a handheld consumer device; labels come from the device's ECG algorithms. AnyPPG (arXiv 2511.01747, supplement) describes it. The device itself is validated in Wang H.","signals":["ppg","ecg"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"Private / proprietary (HeartVoice Medical Technology). Peking University ethics IRB00001052-23071. No public release.","gated":true,"wrist":false,"source_url":"https://doi.org/10.48550/arXiv.2511.01747","doi":null,"url":"/research/datasets/wenxinwuyang-handheld-ecg-ppg"},{"slug":"wesad","name":"WESAD","summary":"WESAD (Schmidt / Reiss / Duerichen / Marberger / Van Laerhoven), ICMI 2018. Lab study with baseline / stress / amusement states plus questionnaires.","signals":["ppg","ecg","accel","skin-temp","eda","resp","emg"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":15,"license":"Scientific, non-commercial use with credit to the owners","gated":null,"wrist":true,"source_url":"https://ubicomp.eti.uni-siegen.de/home/datasets/icmi18/","doi":null,"url":"/research/datasets/wesad"},{"slug":"wf-ppg","name":"WF-PPG","summary":"Ho, Pham, Saeed, Ma. WF-PPG: A Wrist-finger Dual-Channel Dataset for Studying the Impact of Contact Pressure on PPG Morphology. Scientific Data (2025), doi:10.1038/s41597-025-04453-7.","signals":["ppg","ecg","spo2","contact-pressure"],"ppg_sites":["finger","wrist"],"ppg_wavelengths_nm":null,"bp_reference":["cuff"],"subjects":27,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://doi.org/10.6084/m9.figshare.27011998","doi":"10.6084/m9.figshare.27011998","url":"/research/datasets/wf-ppg"},{"slug":"whamap","name":"WHAMAP","summary":"WHAMAP (Silva / Silva / Oliveira, 2026). Mendeley Data version 1. DOI 10.17632/8zt9g9g64c.1.","signals":["accel","gyro"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":10,"license":"CC BY 4.0","gated":null,"wrist":false,"source_url":"https://data.mendeley.com/datasets/8zt9g9g64c/1","doi":"10.17632/8zt9g9g64c.1","url":"/research/datasets/whamap"},{"slug":"wharf","name":"WHARF","summary":"Labelled recordings from a single wrist-worn tri-axial accelerometer for simple ADL primitives. No licence file. Awesome list: 16 volunteers.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":null,"license":"none in repo (all rights reserved by default)","gated":null,"wrist":null,"source_url":"https://github.com/fulviomas/WHARF","doi":null,"url":"/research/datasets/wharf"},{"slug":"wildppg","name":"WildPPG","summary":"ETH Zürich (Siplab) real-world PPG dataset — NeurIPS 2024 Datasets & Benchmarks. Four custom devices worn simultaneously at head, sternum, wrist, ankle during a full-day outdoor trip from Zurich to Jungfraujoch (up to 3,571 m elevation).","signals":["ppg","ecg","accel","skin-temp","baro"],"ppg_sites":["ankle","chest","forehead","wrist"],"ppg_wavelengths_nm":"green 530 / ir 950 / red 660 nm","bp_reference":[],"subjects":16,"license":"CC BY-NC-SA 4.0","gated":null,"wrist":true,"source_url":"https://siplab.org/projects/WildPPG","doi":null,"url":"/research/datasets/wildppg"},{"slug":"wimid","name":"WIMID","summary":"WIMID (Wrist IMU Meals and Intakes Dataset) from Jožef Stefan Institute (Jordan / Luštrek). Zenodo 10.5281/zenodo.18176217. Preprocessed IMU (interpolated to 100 Hz, median-filtered).","signals":["accel","gyro"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":11,"license":"CC BY 4.0","gated":true,"wrist":true,"source_url":"https://zenodo.org/records/18176217","doi":"10.5281/zenodo.18176217","url":"/research/datasets/wimid"},{"slug":"wisdm","name":"WISDM AR v1.1","summary":"Wireless Sensor Data Mining (WISDM) Lab, Fordham University. Activity Prediction / AR dataset v1.1 corresponds to Kwapisz et al., Activity Recognition using Cell Phone Accelerometers (SensorKDD 2010). Fordham dataset page hosts raw + transformed files; user IDs masked 1–36.","signals":["accel"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":36,"license":null,"gated":null,"wrist":false,"source_url":"https://www.cis.fordham.edu/wisdm/dataset.php","doi":null,"url":"/research/datasets/wisdm"},{"slug":"wrist-ppg-exercise","name":"Wrist PPG During Exercise (PhysioNet wrist)","summary":"PhysioNet wrist PPG during exercise corpus (wrist 1.0.0). Treadmill walk/jog and bike resistance protocols with synchronized chest ECG R-peaks.","signals":["ppg","ecg","accel","gyro"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":8,"license":"ODC-By 1.0","gated":null,"wrist":true,"source_url":"https://physionet.org/content/wrist/1.0.0/","doi":"10.13026/C2PQ1X","url":"/research/datasets/wrist-ppg-exercise"},{"slug":"yoga4all-surya-namaskar-wrist-imu","name":"Yoga4All","summary":"Jobs: har.","signals":["accel","gyro","magnetometer"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":21,"license":"CC BY 4.0","gated":null,"wrist":true,"source_url":"https://ieee-dataport.org/documents/yoga4all-end-end-framework-collecting-wearable-sensor-datasets-recognize-and-monitor","doi":"10.21227/prkc-h546","url":"/research/datasets/yoga4all-surya-namaskar-wrist-imu"},{"slug":"yunnan-dual-wavelength-ppg-ecg-pressure-pulse-wave","name":"Yunnan dual-wavelength PPG + ECG + pressure pulse wave (102 subj, 1000 Hz)","summary":"Data descriptor-style release; quality checked by HR-consistency screening (accuracy 86.63%, precision 100%, per record).","signals":["ppg","ecg","pressure"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":["cuff"],"subjects":102,"license":"CC BY 4.0","gated":null,"wrist":null,"source_url":"https://figshare.com/articles/dataset/_b_Dataset_for_Synchronous_Dual_wavelength_Photoplethysmography_Electrocardiography_and_Pulse_Wave_b_/33033332/1","doi":"10.6084/m9.figshare.33033332.v1","url":"/research/datasets/yunnan-dual-wavelength-ppg-ecg-pressure-pulse-wave"},{"slug":"zchsound","name":"ZCHSound","summary":"Paediatric heart-sound (PCG) recordings from 1,259 participants (newborn–14 y, 3 children's hospitals), normal vs CHD subtypes (VSD, ASD, PDA…), split into clean and noisy subsets. IEEE TBME 2024. GitHub mirror (WeiJieOvO/ZCHSound-Dataset) defers usage terms to the paper/site.","signals":["audio"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":1259,"license":null,"gated":null,"wrist":false,"source_url":"http://zchsound.ncrcch.org.cn/","doi":"10.1109/TBME.2023.3348800","url":"/research/datasets/zchsound"},{"slug":"zu-pwd-25","name":"ZU-PWD '25","summary":"ZU-PWD ’25 (DOI 10.21227/2tth-wj12) releases synchronized minute-level physiology/behavior from Empatica Embrace Plus with a companion self-report set (ZU-PWD-SR ’25). Authors: Evangelista / Nazir / Bukhari / Dahmani / Tbaishat / Sharma (Zayed University et al.). Created 2025-09-16 on IEEE DataPort; last updated 2025-09-24.","signals":["accel","skin-temp","eda","resp","hr","hrv"],"ppg_sites":[],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":28,"license":null,"gated":null,"wrist":true,"source_url":"https://ieee-dataport.org/documents/zayed-university-physiological-wellness-dataset-2025-zu-pwd-25","doi":"10.21227/2tth-wj12","url":"/research/datasets/zu-pwd-25"},{"slug":"udpad-wrist-ppg-imu-microgesture","name":"μDPad","summary":"The authors built a dataset because commercial watches with similar multi-photodiode optical layouts don't expose raw channels. The research question is whether spatial PPG picks up the small tissue/tendon deformations under the watch during finger microgestures.","signals":["ppg","accel","gyro"],"ppg_sites":["wrist"],"ppg_wavelengths_nm":null,"bp_reference":[],"subjects":65,"license":"CC BY-NC-ND 4.0","gated":null,"wrist":true,"source_url":"https://huggingface.co/datasets/eth-siplab/microdpad","doi":"10.1145/3776574.3831197","url":"/research/datasets/udpad-wrist-ppg-imu-microgesture"}],"count":219}