A control strategy for ergometry cycling in rehabilitation robots

Massoud, Rasha; Long, Cheng; Tokhi, M. O.; Gharooni, C.S.

doi:10.1109/romoco.2005.201398

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…Sim et al at Maryland University in USA predicted the maximum speed of bicycle riding by detecting muscle signals, and proposed a neural network-based multiple-input multiple-output controller to improve the coordination of riding [ 13 ]. Massoud et al at the University of Sheffield in United Kingdom proposed PID controllers with different parameters for 6 amputees in the trajectory tracing of bicycle riding [ 14 ]. Maaref et al at University of Alberta in Canada presented a bicycle-type rehabilitation training robot.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Gao

et al. 2020

Sensors

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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 was 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.

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Section: Introductionmentioning

confidence: 99%