This data was collected to provide scientists with a large human gait database conducted in real-world conditions suitable to be used in deep learning algorithms for various human gait analysis applications such as Health, Biometrics, Education, etc.
200GaitData: Gait Analysis and Recognition Based on Smartphone Inertial Sensors to Study Individual Differences in Over Ground Walking
The goal of this study is to introduce a comprehensive gait database of 100 human subjects who walked between two endpoints during two different sessions (200 walking sessions) and their gait data were recorded using two smartphones, one that was attached to their right thigh and another one was carried by a phone holder on the left side of their waist. The metadata including age, gender, smoking, daily exercise time, height, and weight of each individual was also recorded.
Akram Bayat, Amir Vajdi, Mohammad Reza Zaghian, Saman Farahmand, Elham Rastegar, Kian Maroofi, Shaohua Jia, Marc Pomplun
This project was proposed and led by Dr. Akram Bayat under the supervision of Professor Marc Pomplun in her final year as a Ph.D. student at the University of Massachusetts Boston. This project was partially supported by a GSA grant that was given to Akram Bayat.
IRB approval: This Project has been reviewed and approved by the University of Massachusetts Boston IRB, Assurance # FWA00004634.
Data: In order to download data, please use the following link.
Download 200GaitData
Download MetaData
Please cite our Data Description paper on arXiv Human Gait Database for Normal Walk Collected by Smartphone Accelerometer:
@article{vajdi2019human,
title={Human gait database for normal walk collected by smart phone accelerometer},
author={Vajdi, Amir and Zaghian, Mohammad Reza and Farahmand, Saman and Rastegar, Elham and Maroofi, Kian and Jia, Shaohua and Pomplun, Marc and Haspel, Nurit and Bayat, Akram},
journal={arXiv preprint arXiv:1905.03109},
year={2019}
}