Published December 21, 2022 | Version 3
Dataset Open

DUO-GAIT: A Gait Dataset for Walking under Dual-Task and Fatigue Conditions with Inertial Measurement Units

  • 1. Hasso Plattner Institute, University of Potsdam
  • 2. University of Freiburg, University of Potsdam
  • 3. University of Freiburg

Description

Details of the dataset are described in this publication.

In recent years, there has been a growing interest to develop and evaluate gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in non-fatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.

 

----------

Version History

Version 3: Align the last few seconds of recording in raw data of sub_07, dual-task (this does not change any of the walking/exercise sensor signals or the rest of the dataset). Remove the .DS_Store files.

Version 2: Update the license.

Version 1: The original upload.

Files

interim.zip

Files (6.4 GB)

Name Size Download all
md5:de9ffa7ed1d9ea91e0b6b2e3af5ecda1
3.0 GB Preview Download
md5:59262239edaab480115ee5dd553587ee
2.6 MB Preview Download
md5:ce0e6752d990b0f40b4b27ea817a9424
3.4 GB Preview Download

Additional details

Related works

References
Dataset: 10.5281/zenodo.5070771 (DOI)
Journal article: 10.3390/data6090095 (DOI)
Dataset: 10.5281/zenodo.10534054 (DOI)