The prediction of neck extensor force using surface electromyography

Swaminathan, Ramesh, Williams, Jonathan M., Jones, Michael D. and Theobald, Peter ORCID: https://orcid.org/0000-0002-3227-7130 2016. The prediction of neck extensor force using surface electromyography. Journal of Back and Musculoskeletal Rehabilitation 29 (2) , pp. 279-285. 10.3233/BMR-150626

Full text not available from this repository.

Abstract

BACKGROUND: The relationship between muscular force and electromyography (EMG) has been investigated by numerous researchers. EMG has not previously been used as a means of estimating force in the cervical erector spinae (CES). OBJECTIVE: Use EMG of the CES musculature to indirectly predict neck extension force. METHODS: Isometric contractions of the CES muscles were studied at increasing levels of contractile force across all participants (n= 12) to produce an individualised force-EMG relationship. The method of least squares was used to determine the linear regression trend line for the force-EMG relationship. The validity of these individual `correlation curves' was demonstrated through further, blinded, investigation. RESULTS: A linear relationship was identified for the individualised correlation curves that gained in strength for < 50% maximum voluntary contraction (MVC; R2> 0.8 for 80% of trials). The prediction of muscle force from the correlation curves was found to be statistically similar to the equivalent experimental data (p> 0.05). Given the tendency of EMG to slightly overestimate force in most cases, an adjustment coefficient was calculated to reduce the error in the predicted force data. CONCLUSIONS: This study reports a validated method using EMG to indirectly acquire CES muscular force, which has application for clinicians and research scientists working in fields including sport and rehabilitation.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	IOS Press
ISSN:	1053-8127
Last Modified:	01 Nov 2022 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/92765

Citation Data

Cited 4 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item