FlexAssist: A Sensor-based Glove for Hand Function Assessment using MAS and QUEST

Rehabilitation for hand motor impairments, such as those resulting from cerebral palsy or stroke, necessitates precise assessment and targeted feedback. This study develops a wearable device for hand rehabilitation that utilizes flex sensors, Arduino Uno, servo motors, and coin vibration motors. The device captures flex sensor data, records it in a CSV file, and processes it offline to classify spasticity and motor functionality. It applies percentage-based thresholds mapped to the Modified Ashworth Scale (MAS) and the Quality of Upper Extremity Skills Test (QUEST). The values after processing help with servo motor adjustments and vibration patterns to deliver feedback, this provides valuable insights for therapy. This cost-effective and portable system helps in addressing limitations of traditional rehabilitation methods and helps in enhancing sensor-based assessment to provide a scalable approach in improving hand motor function.