PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic ExoskeletonsReport as inadecuate


PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons


PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons - Download this document for free, or read online. Document in PDF available to download.

1

State Key Laboratory of Robotics and System, Harbin Institute of Technology HIT, Harbin 150001, China

2

Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China





*

Author to whom correspondence should be addressed.



Academic Editor: Dan Zhang

Abstract 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. View Full-Text

Keywords: SVM; PSO; locomotion mode identification; feature extraction; MVA; rehabilitation exoskeleton SVM; PSO; locomotion mode identification; feature extraction; MVA; rehabilitation exoskeleton





Author: Yi Long 1, Zhi-Jiang Du 1, Wei-Dong Wang 1, Guang-Yu Zhao 2, Guo-Qiang Xu 2, Long He 2, Xi-Wang Mao 2 and Wei Dong 1,*

Source: http://mdpi.com/



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