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Abstract Pathological tremors significantly affect the quality of life for patients worldwide. Rehabilitation exoskeletons serve as one of the solutions to alleviate these pathological tremors, and voluntary motion prediction-based motion planning has been employed to enhance the performance of these devices. This paper presents a method for predicting future voluntary movement in tremor-alleviating rehabilitation exoskeletons that use voluntary motion prediction-based motion planning. In this study, a Convolutional Neural Network and Transformer architecture based neural network work with EMG sensors to predict future voluntary movements. The results show that approach performs well in predicting future voluntary movements, but there is still a limitation to filter out the tremors completely. In summary, we provide a concept for predicting future voluntary movement, which has the potential to improve the effectiveness of rehabilitation exoskeletons in tremor alleviation. 

Abstract Pathological tremor is a common neuromuscular disorder that significantly affects the quality of life for patients worldwide. With recent developments in robotics, rehabilitation exoskeletons serve as one of the solutions to alleviate these tremors. Accurate predictive modeling of tremor signals can be used to provide alleviation from these tremors via various currently available solutions like adaptive deep brain stimulation, electrical stimulation and rehabilitation orthoses, motivating us to explore better modeling of tremors for long-term predictions and analysis. This study is a preliminary step towards the prediction of tremors using artificial neural networks using EMG signals, leveraging the 20–100 ms of Electromechanical Delay. The kinematics and EMG data of a publicly available Parkinsonian tremor dataset is first analyzed, which confirms that the underlying EMGs have similar frequency composition as the actual tremor. 2 hybrid CNN-LSTM based deep learning architectures are then proposed to predict the tremor kinematics ahead of time using EMG signals and tremor kinematics history, and the results are compared with baseline models. The motivation behind hybrid CNN-LSTM models is to exploit both the temporal and spatial dependencies using CNN and LSTM respectively. This is then further extended by adding constraints-based losses in an attempt to further improve the predictions.