Med Biol Eng Comput - Estimation of elbow flexion force during isometric muscle contraction from mechanomyography and electromyography.

Tópicos

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Resumo

Mechanomyography (MMG) is the muscle surface oscillations that are generated by the dimensional change of the contracting muscle fibers. Because MMG reflects the number of recruited motor units and their firing rates, just as electromyography (EMG) is influenced by these two factors, it can be used to estimate the force exerted by skeletal muscles. The aim of this study was to demonstrate the feasibility of MMG for estimating the elbow flexion force at the wrist under an isometric contraction by using an artificial neural network in comparison with EMG. We performed experiments with five subjects, and the force at the wrist and the MMG from the contributing muscles were recorded. It was found that MMG could be utilized to accurately estimate the isometric elbow flexion force based on the values of the normalized root mean square error (NRMSE = 0.131 ? 0.018) and the cross-correlation coefficient (CORR = 0.892 ? 0.033). Although MMG can be influenced by the physical milieu/morphology of the muscle and EMG performed better than MMG, these experimental results suggest that MMG has the potential to estimate muscle forces. These experimental results also demonstrated that MMG in combination with EMG resulted in better performance estimation in comparison with EMG or MMG alone, indicating that a combination of MMG and EMG signals could be used to provide complimentary information on muscle contraction.

Resumo Limpo

mechanomyographi mmg muscl surfac oscil generat dimension chang contract muscl fiber mmg reflect number recruit motor unit fire rate just electromyographi emg influenc two factor can use estim forc exert skelet muscl aim studi demonstr feasibl mmg estim elbow flexion forc wrist isometr contract use artifici neural network comparison emg perform experi five subject forc wrist mmg contribut muscl record found mmg util accur estim isometr elbow flexion forc base valu normal root mean squar error nrmse crosscorrel coeffici corr although mmg can influenc physic milieumorpholog muscl emg perform better mmg experiment result suggest mmg potenti estim muscl forc experiment result also demonstr mmg combin emg result better perform estim comparison emg mmg alon indic combin mmg emg signal use provid complimentari inform muscl contract

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