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Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization

Lookup NU author(s): Dr Jie ZhangORCiD

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Abstract

A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter is used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.

Publication metadata

Author(s): Li X, Liu Z, Gao X, Zhang J

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2020

Volume: 20

Issue: 22

Online publication date: 15/11/2020

Acceptance date: 12/11/2020

Date deposited: 19/01/2021

ISSN (electronic): 1424-8220

Publisher: MDPI

URL: https://doi.org/10.3390/s20226533

DOI: 10.3390/s20226533

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