Feature Selection of Input Variables for Intelligence Joint Moment Prediction Based on Binary Particle Swarm Optimization

Joint moment is an important parameter for a quantitative assessment of human motor function. However, most existing joint moment prediction methods lacking feature selection of optimal inputs subset, which reduced the prediction accuracy and output comprehensibility, increased the complexity of the input sensor structure, making the portable prediction equipment impossible to achieve. To address this problem, this paper develops a novel method based on the binary particle swarm optimization (BPSO) with the variance accounted for (VAF) as fitness function to reduce the number of input variables while improves the accuracy in joint moment prediction. The proposed method is tested on the experimental data collected from ten healthy subjects who are running on a treadmill with four different speeds of 2, 3, 4 and 5m/s. The BPSO is used to select optimal inputs subset from ten electromyography (EMG) data and six joints angles, and then the selected optimal inputs subset be used to train and predict the joint moments via artificial neural network (ANN). Prediction accuracy is evaluated by the variance accounted for (VAF) test between the predicted joint moment and multi-body dynamics moment. Results show that the proposed method can reduce the number of input variables of five joint moment from 16 to less than 11. Furthermore, the proposed method can better predict joint moment (mean VAF. 94.40 +/- 0 .84%) in comparison with the state-of-the-art methods, i.e. Elastic Net (mean VAF. 93.38 +/- 0 .96%) and mutual information (mean VAF. 86.27 +/- 1.41%). In conclusion, the proposed method reduces the number of input variables and improves the prediction accuracy that may allow the future development of a portable, non-invasive system for joint moment prediction. As such, it may facilitate real-time assessment of human motor function.