Method and Algorithms for Decoding Electrophysiological Signals in Biotechnical Systems of Rehabilitation Type
Purpose of research. Development of a method for decoding electromyosignals in control systems for exoskeletons with virtual reality, allowing to adapt the rehabilitation program of a robotic device to the functional state of the patient. Methods. To restore the motor functions of the lower extremities of post-stroke patients, it is proposed to use a biotechnical system with a robotic device, the control of which is based on the analysis and classification of electromyosignals. The robotic device is controlled by a fuzzy neural network. The formation of the vector of informative features for the neural network is carried out by means of a multilevel comparator, the number of levels of which is determined by the dimension of the vector of informative features determined by averaging the outputs of the comparators in a sliding window. The electromyosignal decoder includes a series-connected block of comparators, a block for calculating informative features, a multiplexer, a first neural network, a memory block and a second neural network, the outputs of which are intended to be connected to a servo motor controller, and a synchronizer connected by an output to the control inputs of the multiplexer, memory unit and servo motor controller. Results. A classifier of electromyosignals has been developed, which is characterized by the use of multiple duplicate channels of EMG signals associated with a muscle or muscle groups that control the movement of the same joint of the extremities, as a result of which, at the output of the classifier of each channel, we obtain a number corresponding to the confidence in the command to rotate the servo motor of the exoskeleton, all the outputs of the channel classifiers are fed to a fuzzy neural network, the defuzzifier of which generates a control signal to the servo motor controller. In the course of the work, a software application was written that can control the exoskeleton using the analysis of electromyosignals. Conclusion. The study showed that it is possible to change the indicators of clinical outcome in patients with subacute stroke experience after 12 sessions of BPS training. A biotechnical system with fuzzy control of a robotic device allows for an individual strategy for the rehabilitation of post-stroke patients (including targeted walking training). SHAPE \* MERGEFORMAT