Assistive Soft Robotic Glove Intervention Using Brain-Computer Interface for Elderly Stroke Patients

Hand motor impairment is very common after a stroke. These impairments include difficulty moving and coordinating the hands and fingers, which inhibits stroke patients from being able to perform daily functional tasks independently, resulting in a reduced quality of life. More than half of people with upper limb impairment after stroke will still have problems many months to years after their stroke. Therefore, improving hand function is a core element of rehabilitation. Many possible interventions have been developed; these may involve different exercises or training, specialist equipment or techniques, or they could take the form of a drug (pill or injection) given to help hand movement. There is limited evidence that suggests the following interventions may be effective: constraint-induced movement therapy, mental practice, mirror therapy,interventions for sensory impairment, virtual reality and a relatively high dose of repetitive task practice. Current hand rehabilitation robotic devices are typically driven by rigid linkages or joints, which subject the patient's fingers into a single plane of motion that will feel unnatural and uncomfortable. On top of that,these devices belong to the class of continuous passive motion (CPM) devices that only promote hand range-of-motion, but do not require the patient to play a semi-active role in performing the hand exercises. Furthermore, there is a huge demand for solutions assisting stroke patients with using the densely paralyzed hand to perform activities of daily living (ADL) in real life, which is not available at present. Most of the hand rehabilitation robotic devices available in the market cannot assist paralyzed hand to carry out ADL. To develop an assistive device to solve this unmet need, we decided to combine BCI technology with the wearable soft robotic glove, which enables actuation of paralyzed hand by motor imagery.