Functional electrostimulation system for rehabilitation of the human hand using electromyography signal classification by artificial neural networks

Abstract

The human hands serve as a vital interface through which individuals perceive and interact with the world, making them the earliest means of communication and artistic expression. Upper limb mobility impairments primarily result from accidents or strokes, frequently afflicting individuals in their productive years. Such impairments not only hinder physical functions but also exact a profound psychological toll as individuals deal with the loss of autonomy. The rehabilitation process, although indispensable, often appears monotonous and useless, leading to frustration and disengagement for both patients and caregivers. In response to this challenge, integrating technological tools into rehabilitation therapies has become more relevant to enhance the efficiency and safety of rehabilitation. One promising approach is the utilization of functional electrostimulation, which stimulates the human hand during the therapy to execute the desired movements. Due to this aid, the rehabilitation becomes less demanding and more efficient. This work compares different literature, where all the reviewed papers state that functional electrostimulation is efficient in improving muscle strength, upper limb function, and reducing pain and spasticity. Nevertheless, there remains a crucial gap in the field, defining the appropriate voltage-current amplitude for the stimulation signal. Existing studies have explored the morphology and frequency of the signal, leaving the signal amplitude and even the therapy time at the user’s discretion. To achieve this, the morphology of the electromyographic signals coming from the upper limb was studied in order to extract the most important characteristics and, thus, through a Long-Short Term Memory (LSTM) with an accuracy of 91.87% , identify which movement they corresponded to. The trajectory movements of a ealthy person used as a reference, were then compared with that of the patient requiring stimulation in order to obtain the differential error between the two of them. Based on the error vector found, we used a second LSTM with a regression layer to calculate the exact voltage amplitude the patient would need to vercome the missing voltage differential so that he or she could replicate the movement as similar as possible to the reference one. By addressing this critical aspect, the research aims to introduce an innovation of the current methods for upper limb rehabilitation, offering a more efficient approach to generating functional electroestimulation signals that could be used in human extremities rehabilitation.