Use of predictive models based on biomedical signals and motion measurements for predicting extremity kinematics

Abstract: Due to staff shortages among physiotherapists and an ageing society, there is a growing need for the dynamic development of robot-aided rehabilitation. An ideal solution is a therapy conducted remotely, requiring minimal supervision by a physiotherapist, thus saving time and increasing the number of people treated. To achieve this, a rehabilitation device equipped with intelligent systems to detect dangerous situations for patients is essential. The paper presents a methodology for constructing a predictive model for a control system dedicated to home kinesiotherapy with an exoskeleton. It involves NARX-type recurrent neural networks based on the patients' electromyographic (EMG) measurements while exercising. Within the study, simultaneous EMG measurements and motion capture of the upper extremity were performed on three participants. The collected data were divided into sets for learning and testing neural networks. The kinematics was calculated using a multibody model of the upper limb with five degrees of freedom. The position data obtained from markers were converted into joint angles. Subsequently, a neural network was modelled in MATLAB, with the EMG measurements as inputs and the rotation angles in the upper limb joints as outputs. A sequence of movements covering the entire workspace of the upper limb was adopted as the network training set, while the network's performance was evaluated based on trajectory data from five simple exercises. The reported accuracy of the results remained within the range of 0.05-1°. The study revealed differences in the quality of the result depending on whether the participant of the exercise changes between the training and validation. To optimize predictions and reduce computation time, several different networks with varying parameters were constructed, trained and compared. The quasi-optimal parameters of the models were identified, including the number of hidden neurons, samples of previous output signal values, and samples of prior input signal values.

Keywords: electromyography, motion-capture, recurrent neural networks, robotic-aided kinesiotherapy, therapy safety, hazards detection

DOI: 10.54941/ahfe1004361

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