Next Article in Journal
Dynamic Aberration Correction for Conformal Window of High-Speed Aircraft Using Optimized Model-Based Wavefront Sensorless Adaptive Optics
Previous Article in Journal
Low-Rank Matrix Recovery Approach for Clutter Rejection in Real-Time IR-UWB Radar-Based Moving Target Detection
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(9), 1408;

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

State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China
Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China
Author to whom correspondence should be addressed.
Academic Editor: Dan Zhang
Received: 28 June 2016 / Revised: 9 August 2016 / Accepted: 10 August 2016 / Published: 2 September 2016
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [5284 KB, uploaded 2 September 2016]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Long, Y.; Du, Z.-J.; Wang, W.-D.; Zhao, G.-Y.; Xu, G.-Q.; He, L.; Mao, X.-W.; Dong, W. PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons. Sensors 2016, 16, 1408.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top