Myoelectric control is used in assistive devices such as powered prostheses or exoskeletons, and advances in this area could greatly improve the quality of life for individuals with motor disabilities. Regression machine learning models have been studied to provide a more natural and intuitive control of these devices. Such models have often been applied to estimate joint kinematic variables like angles, angular velocities, and accelerations, with the former receiving considerably more attention in the literature. Despite this prevailing focus, we proposed that employing velocities or accelerations as the target variable of myoelectric regressors instead of angles could lead to better regression performances. To investigate this, our study compares the performance of electromyogram (EMG) regressors in predicting each of these three joint kinematic variables. A comprehensive comparison was conducted, including different regression models, feature extraction methods, conditions, joints, and performance metrics. Overall, the performances of models estimating joint angular velocities or accelerations were consistently superior to those estimating angles. However, the differences between angular velocities and accelerations often were small and lacked statistical significance. These results were observed for most models, features, and conditions, not just the best-performing ones (which were: models = convolutional neural networks; features = waveform length, difference absolute mean value, and Willison amplitude; and conditions = wider and faster movements). Several factors were proposed to explain these findings in the context of current literature, including aspects of the EMG-kinematic signals relationships, kinematic waveform properties, studied movements, and the process for obtaining kinematic variables. Based on the results and discussion, recommendations for future works were made, and areas for further research were suggested, both aiming at improving myoelectric assistive technologies.
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