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IMU-Based Gait Phase Recognition for Stroke Survivors

Published online by Cambridge University Press:  10 April 2019

Yu Lou ,
Rongli Wang ,
Jingeng Mai ,
Ninghua Wang  and
Qining Wang Open the ORCID record for Qining Wang [Opens in a new window]
Yu Lou
Affiliation:
The Robotics Research Group, College of Engineering, Peking University, Beijing 100871, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China
Rongli Wang
Affiliation:
Department of Rehabilitation Medicine, First Hospital, Peking University, Beijing 100034, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China
Jingeng Mai
Affiliation:
The Robotics Research Group, College of Engineering, Peking University, Beijing 100871, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China Beijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT), Peking University, Beijing 100871, China
Ninghua Wang
Affiliation:
Department of Rehabilitation Medicine, First Hospital, Peking University, Beijing 100034, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China
Qining Wang*
Affiliation:
The Robotics Research Group, College of Engineering, Peking University, Beijing 100871, China. E-mails: [email protected], [email protected] Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing 100871, China Beijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT), Peking University, Beijing 100871, China
*
*Corresponding author. E-mail: [email protected]
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Summary

Using wearable robots is an effective means of rehabilitation for stroke survivors, and effective recognition of human motion intentions is a key premise in controlling wearable robots. In this paper, we propose an inertial measurement unit (IMU)-based gait phase detection system. The system consists of two IMUs that are tied on the thigh and on the shank, respectively, for collecting acceleration and angular velocity. Features were extracted using a sliding window of 150 ms in length, which was then fed into a quadratic discriminant analysis (QDA) classifier for classification. We recruited five stroke survivors to test our system. They walked at their own preferred speed on the level ground. Experimental results show that our proposed system has the ability of recognizing the gait phase of stroke survivors. All recognition accuracy results are above 96.5%, and detections are about 5–15 ms in advance of time. In addition, using only one IMU can also give reliable recognition results. This paper proposes an idea about the further research on human–computer interaction for the control of wearable robots.

Keywords

ExoskeletonsHuman biomechanicsMan–machine systemsMechatronic systemsControl of robotic systems
Type
Articles
Information
Robotica , Volume 37 , Special Issue 12: Wearable Robotics: Dynamics, Control and Applications , December 2019 , pp. 2195 - 2208
Copyright
© Cambridge University Press 2019 

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Footnotes

Yu Lou and Rongli Wang contributed equally to this paper.

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