Extreme Gradient Boosting for Limb Position Invariant Myoelectric Pattern Recognition

Abstract

Myoelectric control has a wide range of potential applications including the design of human-machine interfaces for assistive technologies and robotics (prostheses and orthoses) as well as powered exoskeletons. The current work focuses on the Extreme Gradient Boosting - one of the most popular pattern recognition strategies for decoding the information of surface electromyography (sEMG) signals to infer the underlying muscle movements. In the EMG signal based controller design, it is now quite well-established that the position invariant model is a vital aspect. Hence, it should be considered to capture the inevitable dynamic nature of the upper limb. To this end, we have performed the experiments on a dataset consisting of the sEMG signals collected from eleven subjects at five different upper limb positions. The proposed method relies on the pre-processing stage of feature extraction which converts sEMG signal into the correlated time-domain descriptors (cTDD) - a set of descriptive values in the Euclidean space, which helps to learn the gradient boosting classifier. As the EMG signal classification is a subject-specific problem, the classifier has been customized and fine-tuned using the Bayesian optimization method for each subject to get the best possible results. The experimental results have shown that the proposed approach has outperformed the other existing popular classifiers in terms of classification accuracy.