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Dataset

DaRA

warehouse HAR + context: 6 IMUs, BLE beacons, video, 207 labels (n=18, Sensors 2026)

IMURaw waveforms
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DaRA is a logistics activity-recognition dataset recorded in the Fraunhofer IML lab during order picking, packaging, unpacking and storage by 18 subjects. Sensors include Motion Miners IMUs at 100 Hz (two sets of three for each subject), 57 BLE beacons, first-person GoPro cameras, six fixed cameras and warehouse-management scan data, giving about 32 h of recordings and over 109 h of video. Annotations cover 12 class categories with 207 distinct labels.

At a glance
Type
Dataset
Host
Not reported
DOI
10.5281/zenodo.10468175 (opens in a new tab)
Licence
Not reported
Access
Not reported
Subjects
18
Signals
IMU
PPG sites
None
Setting
Not reported
Wrist
Yes
Raw waveforms
Yes
Tasks
Human activity recognition
Updated
Oct 31, 2026

Original description

DaRA is a freely accessible logistics dataset for Human Activity Recognition, Human Context Recognition, and Human Process Recognition. In the laboratory of the Fraunhofer Institute for Material Flow and Logistics (IML), order picking, packaging, unpacking, and storage scenarios were implemented. The lab is a research infrastructure, designed for application-oriented logistics research. It focuses on key questions such as process optimization, logistics IT, human-technology interaction, and ergonomics. The movements and positions of 18 subjects were recorded using:

Sensor / System Manufacturer Frame rate [Hz], [fps] Number of Sensors Explanation

Initial Measurement Units (IMU) Motion Miners  100 18

3 IMUs per set

2 sets per subject

3 subjects per recording

Bluetooth low energy devices (beacons) Motion Miners  10 57 3 dynamic beacons on the picking carts

54 fixed beacons in the experimental area

Action cameras (first-person view) GoPro 12

29.97

3 1 camera per subject

3 subjects per recording

Fixed cameras (third-person view) Mevo from Logitech 29.97 6 6 cameras per recording

Warehouse Management System Logistics Reply

scanning during item retrieval

Each subject was recorded for 1 hour (01:20:41) to over 2.5 hours (02:35:11). In total, all 18 subjects were recorded 32 hours (31:55:26 hours action cameras). With the fixed cameras (77:18:24), a total of over 109 hours (109:13:50) of video data is available.

All sensor data is annotated according to the following human movement and context class categories:

CC01: Main Activity CC02: Sub-Activity - Legs CC03: Sub-Activity - Torso CC04: Sub-Activity - Left Hand CC05: Sub-Activity - Right Hand CC06: Order CC07: Information Technology CC08: High-Level Process CC09: Mid-Level Process CC10: Low-Level Process CC11: Location - Human CC12: Location - Cart

Depending on the class category, between 4 and 35 class labels were defined, yielding a total of 207 distinct labels. When all categories are combined, annotation and revision result in 68,174 unique label representations.

If you use this dataset for research, please cite this dataset and the following paper: "DaRA Dataset: Combining Wearable Sensors, Location Tracking, and Process Knowledge for Enhanced Human Activity and Human Context Recognition in Warehousing", MDPI Sensors, 2026, DOI: 10.3390/s26020739

For any questions about the dataset, please contact Friedrich Niemann at friedrich.niemann@tu-dortmund.de.

Verbatim fromZenodo· Licence: cc-by-nc-4.0

DaRA: ChatPPG research summary card

Key numbers

18
Subjects
1
Reference labels
ACTIVITY

How it works

How DaRA works: Devices & body sites → Signals recorded → TasksDevices & body sites: None (PPG site). Signals recorded: DaRA, 18 subjects, signals IMU. Tasks: Activity recognition.Devices & body sitesNone: PPG siteNonePPG siteSignals recordedDaRA18 subjectsxyzIMUTasksActivity recognitionActivity recognition

No model in the hub reports results on this dataset yet.

Reference labels
activity

Cite this dataset

Cite this dataset with the fields recorded here. Check it against the publisher before submitting.

DaRA [Data set]. (n.d.). https://doi.org/10.5281/zenodo.10468175
@misc{daradara,
  title         = {{DaRA}},
  doi           = {10.5281/zenodo.10468175}
}

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). DaRA: dataset record (Version 2026-10) [Data set]. ChatPPG Research Hub. https://chatppg.com/research/datasets/dara

BibTeX
@misc{chatppg_dataset_dara,
  author       = {{ChatPPG}},
  title        = {{DaRA: dataset record}},
  year         = {2026},
  month        = oct,
  version      = {2026-10},
  howpublished = {\url{https://chatppg.com/research/datasets/dara}},
  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: "DaRA: dataset record"
authors:
  - name: "ChatPPG"
url: "https://chatppg.com/research/datasets/dara"
repository-artifact: "https://chatppg.com/research/snapshots/2026-10"
version: "2026-10"
date-released: "2026-10-31"

History

  1. Record updated

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