Real-time wearable gait phase segmentation for running and walking

Jien De Sui, Wei Han Chen, Tzyy Yuang Shiang, Tian Sheuan Chang

研究成果: 書貢獻/報告類型會議論文篇章

4 引文 斯高帕斯(Scopus)

摘要

Previous gait phase detection as convolutional neural network (CNN) based classification task requires cumbersome manual setting of time delay or heavy overlapped sliding windows to accurately classify each phase under different test cases, which is not suitable for streaming Inertial-Measurement-Unit (IMU) sensor data and fails to adapt to different scenarios. This paper presents a segmentation based gait phase detection with only a single six-axis IMU sensor, which can easily adapt to both walking and running at various speeds. The proposed segmentation uses CNN with gait phase aware receptive field setting and IMU oriented processing order, which can fit to high sampling rate of IMU up to 1000Hz for high accuracy and low sampling rate down to 20Hz for real time calculation. The proposed model on the 20Hz sampling rate data can achieve average error of 8.86 ms in swing time, 9.12 ms in stance time and 96.44% accuracy of gait phase detection and 99.97% accuracy of stride detection. Its real-time implementation on mobile phone only takes 36 ms for 1 second length of sensor data.

原文英語
主出版物標題2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728133201
出版狀態已發佈 - 2020
事件52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Virtual, Online
持續時間: 2020 十月 102020 十月 21

出版系列

名字Proceedings - IEEE International Symposium on Circuits and Systems
2020-October
ISSN(列印)0271-4310

會議

會議52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
城市Virtual, Online
期間2020/10/102020/10/21

ASJC Scopus subject areas

  • 電氣與電子工程

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