Title: Sparse Gaussian process regression in real-time myoelectric control

Authors: Myong Chol Jung; Rifai Chai; Jinchuan Zheng; Hung Nguyen

Addresses: Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia ' Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia ' Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia ' Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia

Abstract: In myoelectric control, nonlinear regression models, Gaussian process (GP) in specific, have shown promising accuracy in estimation, but no study has been conducted to evaluate the real-time performance of GP regression. In this work, the real-time performance of sparse GP regression is evaluated with 17 able-bodied subjects. Unlike the existing training methods, in which training protocols are strictly pre-determined, a novel training method is proposed. The subjects' real-time performance adjusts training time and the number of training samples. While the majority of subjects showed similar learning rates, there was a significant difference between a few subjects (p < 0.05). As a result of real-time performance, the subjects completed 97% of the average tasks and achieved 80% path efficiency comparable to existing methods.

Keywords: sparse Gaussian process regression; regression; real-time myoelectric control; real-time control; myoelectric control; nonlinear regression; electromyography; EMG; human-computer interface; rehabilitation engineering.

DOI: 10.1504/IJMIC.2021.123372

International Journal of Modelling, Identification and Control, 2021 Vol.39 No.1, pp.51 - 60

Received: 18 Nov 2020
Accepted: 25 Jan 2021
Published online: 13 Jun 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article