Multi-mode Stroke Rehabilitation System Using Signal-Controlled Human Machine Interface
- Publication Type:
- Thesis
- Issue Date:
- 2021
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Stroke has become one of the most devastating health problems due to post-stroke disabilities. Rehabilitation is the necessary process for stroke survivors following discharging from the intensive care units. Of those stroke survivors, many cannot regain full arm functions, in turn, their daily lives are dramatically affected since they cannot perform daily activities independently. This thesis proposes a stroke rehabilitation system with multiple training modes using neuroelectric signals. First, motion intent recognition and emotion classification are developed using electroencephalogram signal. The motion intent system recognises the desired motion before execution. At the same time, emotions of the patients are monitored to ensure safety while the patients are doing exercises. Second, electromyogram connectivity analysis using multivariable autoregression is proposed to analyse the inter-relationship between muscles. Using connectivity analysis, the system controls the paretic arm to generate identical connectivity patterns as the non-paretic arm to achieve higher rehabilitation outcome. A wearable exoskeleton is built as a rehabilitation device, which guides and supports rehabilitation movements based on the patients’ physiological signals. Features such as wireless communication, touch screen user interface, etc., are implemented to promotes the ease-of-use and expand the possible applications in the clinical field.
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