On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition

Locomotion mode recognition is one of the key aspects of control of intelligent prostheses. This paper presents a wireless wearable multi-sensor system for locomotion mode recognition. The sensor suit of the system includes three inertial measurement units (IMUs) and eight force sensors. The system...

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Main Authors: Enhao Zheng, Baojun Chen, Xuegang Wang, Yan Huang, Qining Wang
Format: Article
Language:English
Published: SAGE Publishing 2014-02-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.5772/57788
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spelling doaj-27b450a1a5c3473698274224436366232020-11-25T03:09:24ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142014-02-011110.5772/5778810.5772_57788On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand TransitionEnhao Zheng0Baojun Chen1Xuegang Wang2Yan Huang3Qining Wang4 Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Peking University, Beijing, China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Peking University, Beijing, China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Peking University, Beijing, China Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing Institute of Technology, Beijing, China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Peking University, Beijing, ChinaLocomotion mode recognition is one of the key aspects of control of intelligent prostheses. This paper presents a wireless wearable multi-sensor system for locomotion mode recognition. The sensor suit of the system includes three inertial measurement units (IMUs) and eight force sensors. The system was built to measure both kinematic (tilt angles) and dynamic (ground contact forces) signals of human gaits. To evaluate the recognition performance of the system, seven motion modes and sit-to-stand transition were monitored. With a linear discriminant analysis (LDA) classifier, the proposed system can accurately classify the current states. The overall motion mode recognition accuracy was 99.9% during the stance phase and 98.5% during the swing phase. For sit-to-stand transition recognition, the average accuracy was 99.9%. These promising results show the potential of the designed system for the control of intelligent prostheses.https://doi.org/10.5772/57788
collection DOAJ
language English
format Article
sources DOAJ
author Enhao Zheng
Baojun Chen
Xuegang Wang
Yan Huang
Qining Wang
spellingShingle Enhao Zheng
Baojun Chen
Xuegang Wang
Yan Huang
Qining Wang
On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition
International Journal of Advanced Robotic Systems
author_facet Enhao Zheng
Baojun Chen
Xuegang Wang
Yan Huang
Qining Wang
author_sort Enhao Zheng
title On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition
title_short On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition
title_full On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition
title_fullStr On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition
title_full_unstemmed On the Design of a Wearable Multi-sensor System for Recognizing Motion Modes and Sit-to-stand Transition
title_sort on the design of a wearable multi-sensor system for recognizing motion modes and sit-to-stand transition
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2014-02-01
description Locomotion mode recognition is one of the key aspects of control of intelligent prostheses. This paper presents a wireless wearable multi-sensor system for locomotion mode recognition. The sensor suit of the system includes three inertial measurement units (IMUs) and eight force sensors. The system was built to measure both kinematic (tilt angles) and dynamic (ground contact forces) signals of human gaits. To evaluate the recognition performance of the system, seven motion modes and sit-to-stand transition were monitored. With a linear discriminant analysis (LDA) classifier, the proposed system can accurately classify the current states. The overall motion mode recognition accuracy was 99.9% during the stance phase and 98.5% during the swing phase. For sit-to-stand transition recognition, the average accuracy was 99.9%. These promising results show the potential of the designed system for the control of intelligent prostheses.
url https://doi.org/10.5772/57788
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