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

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 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.

Integrated Sensor Fusion and Multi-Modal Hierarchical Neural Network for Activity Recognition in Lower Limb Prosthetics

“…In 2020, Li et al [16] proposed a unique approach for identifying bicycling stages in lower limb amputees utilizing a Support Vector Machine (SVM) enhanced by Particle Swarm Optimization (PSO). Wireless accelerometers then knee joint angle sensor in prosthesis collect information, filtered with a soft-hard threshold to reduce noise.…”

Torque Measurement and Control for Electric-Assisted Bike Considering Different External Load Conditions

“…References Torque control for assisted E-bike [1,2,24,38,39] Instantaneous pedaling torque waveform [4][5][6][7] Pedaling torque component analysis [8][9][10][11][12][13][14] Factors affecting riding [15][16][17][18][19][20][21][22][23][25][26][27][28][29][30][31][32][33][34][35]] Recharge control for assisted E-bike [36,37] To overcome these limitations on existing E-bike assisted torque control, this paper's motivation is to find the best-suited assisted torque considering external loads. It is shown that the overall cycling torque is affected by external loads, including the cyclist's weight, wind resistance, rolling resistance, and the road slope.…”

“…In [ 26 ], a comprehensive monitoring system was developed. This system integrates environmental factors [ 27 , 28 ], the cyclist’s heart rate [ 29 , 30 ] and respiratory rate [ 31 ], power consumption [ 32 ] and electromyogram [ 33 , 34 ] information, and journey time. Collecting data from the cloud can give the rider a reference indicator to determine the motor power of the journey in order to retain a longer battery life.…”

“…By contrast, [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ] investigate the cyclist’s pedaling dynamic with no motor-assisted torque assumed. Moreover, [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] focus on E-bike cycling performance with respect to human behaviors, including heartbeat, gender, and weight. The authors of [ 40 , 41 ] further evaluate recharge control for assisted motor output.…”

Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees

“…Most of them have lost their lower limbs due to natural disasters, traffic accidents and wars [1,2]. Currently, prostheses are the most significant tools to compensate for the walking function of lower-limb amputees [3][4][5][6][7]. However, most prostheses on the market, either passive or semiactive, cannot realize the joints' active swinging, and thus fail to achieve assisted walking [8].…”