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 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.
State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China. scdxhgd@gmail.com.
Yi
Y
Long
State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China. duzj01@hit.edu.cn.
Zhi-Jiang
ZJ
Du
State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China. wangweidong@hit.edu.cn.
Wei-Dong
WD
Wang
Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China. yuzmoon@126.com.
Guang-Yu
GY
Zhao
Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China. xuguoqiang1988911@163.com.
Guo-Qiang
GQ
Xu
Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China. helong208@126.com.
Long
L
He
Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China. mayu-8714@163.com.
Xi-Wang
XW
Mao
State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China. dongwei@hit.edu.cn.
Wei
W
Dong
D000328:Adult; D000465:Algorithms; D000067190:Exoskeleton Device; D006801:Humans; D008124:Locomotion; D008297:Male; D012046:Rehabilitation; D012371:Robotics; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine
MVA; PSO; SVM; feature extraction; locomotion mode identification; rehabilitation exoskeleton