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

Abstract

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. INDEX TERMS Joint moment prediction, artificial neural network, binary particle swarm optimization, feature selection.

Particle Swarm Optimization: A Comprehensive Survey

“…In another study [181], the authors used the BPSO to address the feature selection problem on input variables for intelligence joint moment prediction. Experimental data gathered from ten electromyography (EMG) data and six joints' angles were used for validation.…”

Serum adipokines/related inflammatory factors and ratios as predictors of infrapatellar fat pad volume in osteoarthritis: Applying comprehensive machine learning approaches

“…Moreover, as the dataset may possess some redundant or irrelevant features, the extraction of coherent information requires a comprehensive search over the sample space, while evolutionary algorithms accelerate the learning process to solve the problem. In order to find the best variable among models for IPFP volume determination with the sub-variables, a PSO-based variable selection algorithm was encoded as such technology has been successfully applied to various areas including feature selection [24][25][26][27] . A PSO algorithm was employed to select the most effective features as it can balance exploration and exploitation in an optimal manner by combining local and global search methods through self and neighboring experiences 28 .…”

Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data

“…In [28], an approach is considered for the joint moment prediction, in which the features are selected using a method based on BPSO and the objective function is based on the variance accounted for (VAF). Similar to the approach from this article, their approach is tested and validated on DLAs data.…”