360 Degree Gait capture: A diverse and multi-modal gait dataset of indoor and outdoor walks acquired using multiple video cameras and sensors

Topham, Luke ORCID: https://orcid.org/0000-0002-6689-7944 and Khan, Wasiq (2022) 360 Degree Gait capture: A diverse and multi-modal gait dataset of indoor and outdoor walks acquired using multiple video cameras and sensors. [Data Collection]

Abstract

Many of the existing gait datasets are limited by their lack of diversity in terms of the participants (e.g., gender, age, height, weight, ethnicity), recording environments (e.g., recording angles, indoors / outdoors), and availability. Therefore, we present a gait dataset containing 65 diverse participants with both indoor and outdoor environments. The data was acquired using 2 digital cameras and a digital goniometer (used to measure joint angles). Each participant provided 24 walking sequences from a range of viewing angles (360 degrees in 45 degree increments). Each participant also provided an alternative outfit to provide diversity in personal appearance.

This dataset will be of value to applications gait identification, human pose estimation, and more.

Additional Information:	Please cite the following paper: Topham, L.K., Khan, W., Al-Jumeily, D., Waraich, A. and Hussain, A.J., 2023. A diverse and multi-modal gait dataset of indoor and outdoor walks acquired using multiple cameras and sensors. Scientific Data, 10(1), p.320. https://doi.org/10.1038/s41597-023-02161-8
Creators:	Topham, Luke and Khan, Wasiq
ORCID:	https://orcid.org/0000-0002-6689-7944UNSPECIFIED
Uncontrolled Keywords:	Gait; Person Identification; Machine Learning
DOI:	https://doi.org/10.24377/LJMU.d.00000133
Division:	Computer Science & Mathematics
Field of Research:	Information and computing sciences
Date Deposited:	28 Oct 2022 14:15
Last Modified:	12 Mar 2024 17:36
Related resources:	Published paper
URI:	https://opendata.ljmu.ac.uk/id/eprint/133
Data collection method:	65 people were recorded for this dataset using 2 digital cameras and a digital goniometer.
Resource language:	English
Metadata language:	English
Statement on legal, ethical and access issues:	UREC Ref: 21/CMP/004 Participants identities such as names are removed and instead data relating to each person is labelled with a participant ID number.
Collection period:	From To 1 October 2021 27 October 2022