Discovery of key surface electromyography features during walking for discerning high and low muscle mass using machine learning analysis.

Low muscle mass is associated with adverse health outcomes not only in older adults, but also in young and middle-aged adults. However, traditional muscle mass assessments rely on specialized equipment and sufficient physical space, limiting their applicability in primary care and community settings. Additionally, machine learning-based approaches suffer from limited interpretability owing to their black-box nature. Therefore, we aimed to evaluate a machine learning-based approach using surface electromyography (sEMG) signals recorded during walking to classify muscle mass levels and identify key features underlying the model’s decisions in young and middle-aged adults. We enrolled 133 community-dwelling adults aged 20–59 years (71 men, 53.4%; 62 women, 46.6%). Appendicular skeletal muscle mass was measured using bioelectrical impedance analysis (InBody 970). Participants were divided into low- and high–muscle-mass groups using unsupervised k-means clustering. Five classifiers (logistic regression, support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting) were trained, and performance was compared for normal and fast walking. Model interpretability was assessed with Shapley additive explanations. The low- and high- muscle-mass groups had mean ages of 36.12 and 39.84 years (p = 0.086), respectively. Extreme gradient boosting achieved the highest accuracy during normal walking (95%), whereas random forest performed best during fast walking (94%). During fast walking, a higher zero crossing rate of the biceps femoris contributed most to classifying higher muscle mass; during normal walking, a lower maximum power of the tibialis anterior was most influential. These findings demonstrate a feasible and interpretable approach to stratifying muscle mass from sEMG during gait. Larger and more diverse dataset are warranted to improve generalizability and support potential clinical application.