DOI: 10.5281/zenodo.10468175
title: Data Fusion for advanced Research in industrial Applications (DaRA) – A Multi-Sensor, Multi-Level Annotated Dataset for Human Activity and Human Context Recognition in Warehousing

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 set2 sets per subject3 subjects per recording



Bluetooth low energy devices (beacons)
Motion Miners 
10
57
3 dynamic beacons on the picking carts54 fixed beacons in the experimental area 


Action cameras (first-person view)
GoPro 12 


29.97

3
1 camera per subject3 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.
description: A detailed description of the dataset can be found in 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

The dataset was created according to the guideline of the following paper: “Towards Standardized Dataset Creation for Human Activity Recognition: Framework, Taxonomy, Checklist, and Best Practices”, ARDUOUS, 2025 DOI: 10.1007/978-3-032-09117-8_5.

The following link contains a Python script for synchronizing sensor data and annotations, as well as a Python script for data pre-processing: https://github.com/wilfer9008/Data4Sim