Impact of load variation on joint angle estimation from surface EMG signals

Tang, Z., Yu, H. and Cang, S. ORCID: 0000-0002-7984-0728, 2016. Impact of load variation on joint angle estimation from surface EMG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24 (12), pp. 1342-1350. ISSN 1534-4320

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Abstract

Many studies use surface electromyogram (sEMG) signals to estimate the joint angle, for control of upper-limb exoskeletons and prostheses. However, several practical factors still affect its clinical applicability. One of these factors is the load variation during daily use. This paper demonstrates that the load variation can have a substantial impact on performance of elbow angle estimation. This impact leads an increase in mean RMSE (Root-Mean-Square Error) from 7.86° to 20.44° in our experimental test. Therefore, we propose three methods to address this issue: 1) pooling the training data from all loads together to form the pooled training data for the training model; 2) adding the measured load value (force sensor) as an additional input; and 3) developing a two-step hybrid estimation approach based on load and sEMG. Experiments are conducted with five subjects to investigate the feasibility of the proposed three methods. The results show that the mean RMSE is reduced from 20.44° to 13.54° using method one, 10.47° using method two, and 8.48° using method three, respectively. Our study indicates that 1) the proposed methods can improve performance and stability on joint angle estimation and 2) sensor fusion (sEMG sensor and force sensor) is an efficient way to resolve the adverse effect of load variation.

Item Type: Journal article
Publication Title: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Creators: Tang, Z., Yu, H. and Cang, S.
Publisher: Institute of Electrical and Electronics Engineers
Date: December 2016
Volume: 24
Number: 12
ISSN: 1534-4320
Identifiers:
NumberType
10.1109/tnsre.2015.2502663DOI
1357089Other
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 27 Aug 2020 09:42
Last Modified: 27 Aug 2020 15:27
URI: http://irep.ntu.ac.uk/id/eprint/40538

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