earticle

영어

This paper introduces a portable flexible wrist rehabilitation sensor system designed to support patients with hand function impairments from stroke or injury. The system collects flexible sensor data before and after rehabilitation to train a specialized AI model, utilizing a Long Short-Term Memory (LSTM) network for real-time analysis. This model evaluates wrist rehabilitation performance and degrees of freedom, using embedded sensors to classify and predict hand movements. Additionally, a user-friendly GUI allows patients to monitor their recovery progress. Compared to traditional rigid exoskeletons, this flexible sensor system offers a comfortable, natural hand simulation, reduces costs, and enhances rehabilitation customization and market viability.