Project page https://cecas.clemson.edu/~ahoover/pedometer/ (updated Nov 2022). Papers: Mattfeld et al., IEEE BIBM 2017 (dataset paper); Mattfeld PhD 2018; Mattfeld, Jesch & Hoover, Evaluating Pedometer Algorithms on Semi-Regular and Unstructured Gaits, Sensors 21(13):4260, 2021 (https://doi.org/10.3390/s21134260). IRB2017-048.
Key numbers
How it works
No model in the hub reports results on this dataset yet.
- Sampling
- Accel 15 Hz (Shimmer3 x3; synchronized + resampled to exactly 15 Hz in Data.zip); raw unsynchronized accel/gyro/mag in RawData.zip (rate per sensor not restated)
- Reference labels
- activity
- Device roles
- Study wearable (IMU)
Devices· 1
Icons show the form factor: we have no openly licensed or manufacturer-cleared photo of these exact devices yet.
Models trained or evaluated on it
FAQ
How many subjects are in Clemson Pedometer Evaluation dataset?
Clemson Pedometer Evaluation dataset has data from 30 subjects, as listed in its research record.
What signals does Clemson Pedometer Evaluation dataset include?
Clemson Pedometer Evaluation dataset includes accelerometer, gyroscope and magnetometer data. Sampling rate as recorded: Accel 15 Hz (Shimmer3 x3; synchronized + resampled to exactly 15 Hz in Data.zip); raw unsynchronized accel/gyro/mag in RawData.zip (rate per sensor not restated). Raw waveforms are included.
What can Clemson Pedometer Evaluation dataset be used for?
Research tasks it supports: human activity recognition. Reference labels: activity.
How do I get Clemson Pedometer Evaluation dataset, and what is its licence?
It is available from https://cecas.clemson.edu/~ahoover/pedometer/. No licence is recorded for it here, so check the terms on the source page before you use it.
Cite
No citation metadata yet.
Cite the ChatPPG record
Cite the dated snapshot 2026-10 for a fixed, reproducible version. Please also cite the original publications listed above.
APA
ChatPPG. (2026). Clemson Pedometer Evaluation dataset: dataset record (Version 2026-10) [Data set]. ChatPPG Research Hub. https://chatppg.com/research/datasets/clemson-pedometer-evaluation
BibTeX
@misc{chatppg_dataset_clemson_pedometer_evaluation,
author = {{ChatPPG}},
title = {{Clemson Pedometer Evaluation dataset: dataset record}},
year = {2026},
month = oct,
version = {2026-10},
howpublished = {\url{https://chatppg.com/research/datasets/clemson-pedometer-evaluation}},
note = {ChatPPG Research Hub, Snapshot 2026-10, https://chatppg.com/research/snapshots/2026-10}
}CITATION.cff
cff-version: 1.2.0 message: "If you use this resource, please cite it as below." type: dataset title: "Clemson Pedometer Evaluation dataset: dataset record" authors: - name: "ChatPPG" url: "https://chatppg.com/research/datasets/clemson-pedometer-evaluation" repository-artifact: "https://chatppg.com/research/snapshots/2026-10" version: "2026-10" date-released: "2026-10-17"
History
Record updated
Papers
ActiNet: an open-source tool for activity intensity classification of wrist-worn accelerometry using self-supervised deep learningSame task: Activity recognitionScientific Reports · 2026
An open-source deep learning model for classifying real-world activity intensities in persons with chronic pain using a wrist-worn accelerometerSame task: Activity recognitionFrontiers in Digital Health 8:1783507 · 2026
AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the WildSame task: Activity recognitionarXiv preprint · 2026
