A preliminary investigation into the analysis of electromyographic activity using a system of multiple neural networks

Caleb, P.; Caleb-Solly, Praminda; Sharpe, P. K.; Jones, R.

Abstract

Electromyography (EMG) is widely used by clinicians and therapists for diagnosis of certain neuromuscular disorders. This paper describes a preliminary examination of the application of multiple neural networks in the analysis of surface electromyographic activity. The multiple neural network system that is currently being developed enables the signal to be actively monitored by tracking changes in clinical assessment indicators such as force levels, dynamic changes in the force and fatigue. The system has been tested on a small number of subjects and shown promising results.

• Presentation Conference Type: Conference Paper (published)
• Publication Date: Dec 1, 1995
• Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 934 LNAI
• Pages: 439-440
• Series Title: Lecture Notes in Computer Science
• Series Number: 934
• DOI: https://doi.org/10.1007/3-540-60025-6_177
• Keywords: electromyographic activity, multiple neural networks
• Public URL: https://uwe-repository.worktribe.com/output/1107258
• Publisher URL: http://dx.doi.org/10.1007/3-540-60025-6_177