Ge, Y., Li, W., Farooq, M. , Qayyum, A., Wang, J., Chen, Z., Cooper, J. , Imran, M. A. and Abbasi, Q. H. (2023) LoGait: LoRa sensing system of human gait recognition using dynamic time wraping. IEEE Sensors Journal, 23(18), pp. 21687-21697. (doi: 10.1109/JSEN.2023.3297438)
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Abstract
Vision-based gait analysis and human identification systems have been widely proposed in the literature. However, these systems cannot be readily applied in many real-time applications due to involved challenges such as video quality, occlusion, and serious privacy concerns. To overcome such issues, we propose the LoGait system that leverages ubiqutous LoRa signals recognise gait in different indoor environments. Our work is based on the intuition that the walking pattern of different users can be distinguished by distinct stride size and frequency. The wireless LoRa signal which is interfered by human walking will capture the gait information of subjects. In combination with the long-distance transmission ability of LoRa signal, the system enables a larger sensing range of gait recognition compared to the WiFi-based gait recognition system. The proposed LoGait system utilizes the phase difference between two LoRa receive channels, along with a set of filtering techniques, to extract distinctive features and generate a human gait profile. This profile is then matched against a database using a dynamic time wraping (DTW) based recognition algorithm, enabling accurate identification based on unique gait patterns. It has been validated in three different scenarios for gait recognition namely line of sight (LOS), non-line of sight (NLOS), and long-distance, with accuracy of 85.13%, 79.14%, and 84.14%, respectively.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Wang, Mr Jingyan and Ge, Yao and Chen, Zikang and Abbasi, Professor Qammer and Farooq, Muhammad and Cooper, Professor Jonathan and Imran, Professor Muhammad and Qayyum, Adnan and Li, Dr Wenda |
| Authors: | Ge, Y., Li, W., Farooq, M., Qayyum, A., Wang, J., Chen, Z., Cooper, J., Imran, M. A., and Abbasi, Q. H. |
| College/School: | College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity College of Science and Engineering > School of Engineering > Biomedical Engineering College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering |
| Journal Name: | IEEE Sensors Journal |
| Publisher: | IEEE |
| ISSN: | 1530-437X |
| ISSN (Online): | 1558-1748 |
| Published Online: | 25 July 2023 |
| Copyright Holders: | Copyright © 2023 IEEE |
| First Published: | First published in IEEE Sensors Journal 2023 |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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