A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration. (May 2019)

• Record Type: Journal Article
• Authors: Bi, Luzheng; Feleke, Aberham -->Genetu; Guan, Cuntai
• Is Part Of: Biomedical signal processing and control. Volume 51(2019)
• Page Start: 113
• Page End: 127
• Publication Date: 2019-05
• Subjects: Electromyography (EMG) -- Human-robot collaboration -- Intention prediction -- Continuous motion -- Upper limb; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.02.011
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Electromyography (EMG) signal is one of the widely used biological signals for human motor intention prediction, which is an essential element in human-robot collaboration systems. Studies on motor intention prediction from EMG signal have been concentrated on classification and regression models, and there are numerous review and survey papers on classification models. However, to the best of our knowledge, there is no review paper on regression models or continuous motion prediction from EMG signal. Therefore, in this paper, we provide a comprehensive review of EMG-based motor intention prediction of continuous human upper limb motion. This review will cover the models and approaches used in continuous motion estimation, the kinematic motion parameters estimated from EMG signal, and the performance metrics utilized for system validation. From the review, we will provide some insights into future research directions on these subjects. We first review the overall structure and components of EMG-based human-robot collaboration systems. We then discuss the state of arts in continuous motion prediction of the human upper limb. Finally, we conclude the paper with a discussion of the current challenges and future research directions.