Identification of forearm skin zones with similar muscle activation patterns during activities of daily living
Journal of neuroengineering and rehabilitation, 2018•Springer
Background 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. Methods 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 …
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. Methods 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 …
Background
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.
Methods
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.
Results
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.
Conclusion
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.
Springer
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