Neck EMG-Based Estimation of Upper-Limb Muscle Tension

Monitoring muscle tension unobtrusively is important for sports and music performance. Conventional approaches estimate bilateral arm exertion by placing electromyography (EMG) sensors on the arms; however, in sports the arms may strike players, risking sensor detachment or damage, and in artistic settings visible arm-mounted sensors may be socially unacceptable. To address these issues, I propose a method that estimates bilateral arm exertion using a single EMG sensor on the posterior neck. The approach leverages the kinetic-chain principle: when the arms tense, cervical muscles co-activate to stabilize posture. Using cervical EMG and machine-learning models, I classify arm tension and regress percent maximum voluntary contraction (%MVC) across both arms. I evaluated the method across five tasks including grip strength, golf putting, and piano playing. The method achieved a mean binary-classification accuracy of 76 %. For regression of mean arm %MVC, it yielded an average RMSE of 10 % and$R^{2}$of 0.72.