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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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 |
work_keys_str_mv |
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