Sparse Gaussian process regression in real-time myoelectric control

作     者：Jung, Myong Chol Chai, Rifai Zheng, Jinchuan Nguyen, Hung 

作者机构：Swinburne Univ Technol Fac Sci Engn & Technol Hawthorn Vic 3122 Australia 

出 版 物：《INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL》 (国际建模、识别与控制杂志)

年 卷 期：2021年第39卷第1期

页      面：51-60页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：sparse Gaussian process regression regression real-time myoelectric control real-time control myoelectric control nonlinear regression electromyography EMG human-computer interface rehabilitation engineering 

摘      要：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.