Identification of forearm skin zones with similar muscle activation patterns during activities of daily living

• Published in: Journal of neuroengineering and rehabilitation Vol. 15; no. 1; p. 91
• Main Authors: Jarque-Bou, Néstor J, Vergara, Margarita, Sancho-Bru, Joaquín L, Roda-Sales, Alba, Gracia-Ibáñez, Verónica
• Format: Journal Article
• Language: English
• Published: England BioMed Central 29.10.2018; BMC
• Subjects: Activation; Activities of daily living; Classification; Cluster analysis; Clustering analysis; Data analysis; Deviation; Electrode placement; Electrodes; Electromyography; Forearm; Forearm muscles; Functional principal component analysis; Muscle contraction; Muscle function; Muscles; Patients; Pattern recognition; Principal components analysis; Prostheses; Rehabilitation; Simulation; Skin; Spots; Wrist; Sollerman hand function test; Electrode placement; Activities of daily living; Myoelectric prostheses; Clustering analysis; Electromyography; Rehabilitation; Forearm muscles; Functional principal component analysis
• ISSN: 1743-0003; 1743-0003
• DOI: 10.1186/s12984-018-0437-0

A deeper knowledge of the activity of the forearm muscles during activities of daily living (ADL) could help to better understand the role of those muscles and allow clinicians to treat motor dysfunctions more effectively and thus improve patients' ability to perform activities of daily living. In this work, we recorded sEMG activity from 30 spots distributed over the skin of the whole forearm of six subjects during the performance of 21 representative simulated ADL from the Sollerman Hand Function Test. Functional principal component analysis and hierarchical cluster analysis (HCA) were used to identify forearm spots with similar muscle activation patterns. The best classification of spots with similar activity in simulated ADL consisted in seven muscular-anatomically coherent groups: (1) wrist flexion and ulnar deviation; (2) wrist flexion and radial deviation; (3) digit flexion; (4) thumb extension and abduction/adduction; (5) finger extension; (6) wrist extension and ulnar deviation; and (7) wrist extension and radial deviation. The number of sEMG sensors could be reduced from 30 to 7 without losing any relevant information, using them as representative spots of the muscular activity of the forearm in simulated ADL. This may help to assess muscle function in rehabilitation while also simplifying the complexity of prosthesis control.