Design and application of pneumatic rehabilitation glove system based on brain–computer interface

Abstract

Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments. Therefore, specific and effective rehabilitation methods seek innovation. To address the overlooked limitation, we design a pneumatic rehabilitation glove system. Specially, we developed a pneumatic glove, which utilizes ElectroEncephaloGram (EEG) acquisition to gain the EEG signals. A proposed EEGTran model is inserted into the system to distinguish the specific motor imagination behavior, thus, the glove can perform specific activities according to the patient's imagination, facilitating the patients with severe movement disorders and promoting the rehabilitation technology. The experimental results show that the proposed EEGTrans reached an accuracy of 87.3% and outperformed that of competitors. It demonstrates that our pneumatic rehabilitation glove system contributes to the rehabilitation training of stroke patients.