Muscle Synergy of Lower Limb Motion in Subjects with and Without Knee Pathology

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2021 Aug 27

PMID 34441253

Citations 1

Authors

Jingcheng Chen

Yining Sun

Shaoming Sun

Affiliations

Soon will be listed here.

Abstract

Surface electromyography (sEMG) has great potential in investigating the neuromuscular mechanism for knee pathology. However, due to the complex nature of neural control in lower limb motions and the divergences in subjects' health and habits, it is difficult to directly use the raw sEMG signals to establish a robust sEMG analysis system. To solve this, muscle synergy analysis based on non-negative matrix factorization (NMF) of sEMG is carried out in this manuscript. The similarities of muscle synergy of subjects with and without knee pathology performing three different lower limb motions are calculated. Based on that, we have designed a classification method for motion recognition and knee pathology diagnosis. First, raw sEMG segments are preprocessed and then decomposed to muscle synergy matrices by NMF. Then, a two-stage feature selection method is executed to reduce the dimension of feature sets extracted from aforementioned matrices. Finally, the random forest classifier is adopted to identify motions or diagnose knee pathology. The study was conducted on an open dataset of 11 healthy subjects and 11 patients. Results show that the NMF-based sEMG classifier can achieve good performance in lower limb motion recognition, and is also an attractive solution for clinical application of knee pathology diagnosis.

Citing Articles

Muscle activation patterns and muscle synergies reflect different modes of coordination during upper extremity movement.

Chen X, Dong X, Feng Y, Jiao Y, Yu J, Song Y Front Hum Neurosci. 2023; 16:912440.

PMID: 36741782 PMC: 9889926. DOI: 10.3389/fnhum.2022.912440.

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