Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction
Joint Authors
Yu, Mei
Li, Guanglin
Huang, Pingao
Wang, Hui
Wang, Yuan
Liu, Zhiyuan
Samuel, Oluwarotimi Williams
Li, Xiangxin
Chen, Shixiong
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-14
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions.
Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems.
Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition.
In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents.
More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements.
Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks.
The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm.
Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased.
These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.
American Psychological Association (APA)
Huang, Pingao& Wang, Hui& Wang, Yuan& Liu, Zhiyuan& Samuel, Oluwarotimi Williams& Yu, Mei…[et al.]. 2020. Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139487
Modern Language Association (MLA)
Huang, Pingao…[et al.]. Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1139487
American Medical Association (AMA)
Huang, Pingao& Wang, Hui& Wang, Yuan& Liu, Zhiyuan& Samuel, Oluwarotimi Williams& Yu, Mei…[et al.]. Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139487
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139487