Hierarchical Projected Regression for Torque of Elbow Joint Using EMG Signals

Predicting Torque by using Electromyography (EMG) signals is a significant problem as long as wearable robots is concerned. In this paper, a coarse-to-fine algorithm, named as Hierarchical Projected Regression, is proposed to estimate the torque of elbow by the surface EMG signals from human's arm. HPR is learned based on a clinical dataset. First, the real torque distribution can be clustered by k-means algorithm. Meanwhile, the EMG signals are divided into multi-clusters corresponding to the partitions of torque dataset. Then, a projected matrix is derived based on the criterion of Linear Discriminant Analysis, by which the low dimensional features are extracted from the original high dimensional EMG. Finally, the process extends to multi-level tree algorithm based on the hierarchical variance reduction of individual torque cluster. The experimental results show the high accuracy and efficiency of this novel method.