PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons
Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transiti...
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doaj-ac81cfe49fea4731bc96ee9d9b3ccc6c2020-11-24T22:17:01ZengMDPI AGSensors1424-82202016-09-01169140810.3390/s16091408s16091408PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic ExoskeletonsYi Long0Zhi-Jiang Du1Wei-Dong Wang2Guang-Yu Zhao3Guo-Qiang Xu4Long He5Xi-Wang Mao6Wei Dong7State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, ChinaWeapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, ChinaWeapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, ChinaWeapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, ChinaWeapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, ChinaLocomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.http://www.mdpi.com/1424-8220/16/9/1408SVMPSOlocomotion mode identificationfeature extractionMVArehabilitation exoskeleton |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Long Zhi-Jiang Du Wei-Dong Wang Guang-Yu Zhao Guo-Qiang Xu Long He Xi-Wang Mao Wei Dong |
spellingShingle |
Yi Long Zhi-Jiang Du Wei-Dong Wang Guang-Yu Zhao Guo-Qiang Xu Long He Xi-Wang Mao Wei Dong PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons Sensors SVM PSO locomotion mode identification feature extraction MVA rehabilitation exoskeleton |
author_facet |
Yi Long Zhi-Jiang Du Wei-Dong Wang Guang-Yu Zhao Guo-Qiang Xu Long He Xi-Wang Mao Wei Dong |
author_sort |
Yi Long |
title |
PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons |
title_short |
PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons |
title_full |
PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons |
title_fullStr |
PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons |
title_full_unstemmed |
PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons |
title_sort |
pso-svm-based online locomotion mode identification for rehabilitation robotic exoskeletons |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2016-09-01 |
description |
Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance. |
topic |
SVM PSO locomotion mode identification feature extraction MVA rehabilitation exoskeleton |
url |
http://www.mdpi.com/1424-8220/16/9/1408 |
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