Combined control of rehabilitation wheelchair using periocular electromyography and electroencephalography

Abstract

To improve the convenience of life for the people with reduced mobility, a combined control method of wheelchair utilizing periocular electromyography (Per-EMG) and electroencephalography (EEG) is presented. Based on the Per-EMG and EEG signals obtained from the bioelectric sensors, a novel feature classification combined model is proposed by combining convolutional neural network (CNN) and long short-term memory (LSTM) neural network. These two deep learning architectures enable the comprehensive analysis and accurate classification of the acquired signals. Then the inferencing results can be converted to the corresponding driving command of the rehabilitation wheelchair. Furthermore, the important metrics such as accuracy, precision and recall are adopted to evaluate the performance of this combined model. These metrics provide a quantitative assessment of the model’s classification capabilities. By practical experiments, the proposed combined control method for rehabilitation wheelchair demonstrates its reasonability and effectiveness. And the wheelchair with combined control method can enhance the mobility and independence of the people with reduced mobility. These findings contribute to the development of assistive technologies in the field of rehabilitation.