Several regression models have been proposed for estimation of isometric joint torque using surface electromyography (SEMG) sig-nals. Common issues related to torque estimation models are degradation of model accuracy with passage of time, electrode displace-ment, and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances, in order to assist researchers with identifying the most appropriate model for a specific biomedical application. Methods Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor, was used to mea-sure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session, for the purpose of evaluating the effects of passage of time and electrode dis-placement on accuracy of models. Acquired SEMG signals were filtered, rectified, normalized and then fed to models for training. Re-sults It was shown that mean adjusted coefficient of determination values decrease between 20%-35% for different models after one hour while altering arm posture decreased mean values between 64% to 74% for different models. Conclusions Model estimation accu-racy drops significantly with passage of time, electrode displacement, and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training time without losing estima-tion accuracy. Among the models compared, ordinary least squares linear regression model (OLS) was shown to have high isometric tor-que estimation accuracy combined with very short training times

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