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Article

Electromyography Pattern Likelihood Analysis for Flexion-Relaxation Phenomenon Evaluation

Department of Information Engineering (DII), Università Politecnica delle Marche, 60131 Ancona, Italy
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Electronics 2020, 9(12), 2046; https://doi.org/10.3390/electronics9122046
Received: 26 October 2020 / Revised: 24 November 2020 / Accepted: 28 November 2020 / Published: 2 December 2020
The myoelectric activity of the back muscles can be studied to evaluate the flexion-relaxation phenomenon and find differences between electromyography patterns on different subjects. In this paper, we propose an algorithm able to provide a myoelectric silence evaluation based on the data acquired from a wireless body sensor network consisting of surface electromyography sensors in association with a wearable inertial measurement unit. From the study group was chosen a gold standard subject, a healthy control with the best regular patterns, as a reference to find a first validity range. Through the subsequent iterations, the range was modified to include the other healthy subjects who showed muscle relaxation according to the previous ranges. Through this likelihood analysis, we want to compare patterns on different channels, identified by the electromyography root mean squared values, to study and find with iterations a validity range for the myoelectric activity silence identification and classification. The proposed algorithm was tested by processing the data collected in an acquisition campaign conducted to evaluate the flexion-relaxation phenomenon on the back muscles of subjects with and without lower back pain. The results show that the submitted method is significant for the clinical assessment of electromyography activity patterns to evaluate which are the subjects that have patterns near or far from the gold standard. This analysis is useful both for prevention and for assessing the progress of subjects with low back pain undergoing physiotherapy. View Full-Text
Keywords: flexion-relaxation phenomenon; surface electromyography; wearable device; automatic detection of the FRP; sEMG patterns; likelihood sEMG analysis flexion-relaxation phenomenon; surface electromyography; wearable device; automatic detection of the FRP; sEMG patterns; likelihood sEMG analysis
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MDPI and ACS Style

Paoletti, M.; Belli, A.; Palma, L.; Pierleoni, P. Electromyography Pattern Likelihood Analysis for Flexion-Relaxation Phenomenon Evaluation. Electronics 2020, 9, 2046. https://doi.org/10.3390/electronics9122046

AMA Style

Paoletti M, Belli A, Palma L, Pierleoni P. Electromyography Pattern Likelihood Analysis for Flexion-Relaxation Phenomenon Evaluation. Electronics. 2020; 9(12):2046. https://doi.org/10.3390/electronics9122046

Chicago/Turabian Style

Paoletti, Michele, Alberto Belli, Lorenzo Palma, and Paola Pierleoni. 2020. "Electromyography Pattern Likelihood Analysis for Flexion-Relaxation Phenomenon Evaluation" Electronics 9, no. 12: 2046. https://doi.org/10.3390/electronics9122046

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