Estimation of EMG-Based force using a neural-network-based approach

Luo, Jing; Liu, Chao; Yang, Chenguang

Abstract

© 2013 IEEE. The dynamics of human arms has a high impact on the humans' activities in daily life, especially when a human operates a tool such as interactions with a robot with the need for high dexterity. The dexterity of human arms depends largely on motor functionality of muscle. In this sense, the dynamics of human arms should be well analyzed. In this paper, in order to analyse the characteristic of human arms, a neural-network-based algorithm is proposed for exploring the potential model between electromyography (EMG) signal and human arm's force. Based on the analysis of force for humans, the mean absolute value of the electromyographic signal is selected as the input for the potential model. In this paper, in order to accurately estimate the potential model, three domains fuzzy wavelet neural network (TDFWNN) algorithm without prior knowledge of the biomechanical model is utilized. The performance of the proposed algorithm has been demonstrated by the experimental results in comparison with the conventional radial basis function neural network (RBFNN) method. By comparison, the proposed TDFWNN algorithm provides an effective solution to evaluate the influence of human factors based on biological signals.

Citation

Luo, J., Liu, C., & Yang, C. (2019). Estimation of EMG-Based force using a neural-network-based approach. IEEE Access, 7, 64856-64865. https://doi.org/10.1109/ACCESS.2019.2917300

• Journal Article Type: Article
• Acceptance Date: May 7, 2019
• Online Publication Date: May 16, 2019
• Publication Date: Jan 1, 2019
• Deposit Date: Oct 29, 2019
• Publicly Available Date: Oct 30, 2019
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 64856-64865
• DOI: https://doi.org/10.1109/ACCESS.2019.2917300
• Keywords: General Engineering; General Materials Science; General Computer Science
• Public URL: https://uwe-repository.worktribe.com/output/4191969
• Publisher URL: https://ieeexplore.ieee.org/document/8716698