IoT-based Physiotherapy Device for Quantifying Hand Condition

This research presents the development and evaluation of an IoT-based physiotherapy device designed to improve the accuracy of hand function assessments for rehabilitation. Traditional physiotherapy practices largely depend on subjective clinician evaluations, leading to inconsistencies and limiting personalized patient progress tracking. To address this, the proposed device uses force-sensitive resistors (FSR), flex sensors, and a gyroscope-accelerometer combination to measure hand grip strength, flexion, and rotation in real-time. The device transmits data wirelessly to a mobile application, providing a continuous, objective, and quantitative approach to monitoring hand functionality. The methodology involved designing and assembling the device components, 3D printing a flexible TPU casing, and developing custom algorithms for processing sensor data via an ESP32 microcontroller. Clinical testing was conducted with diverse participants performing controlled hand exercises, capturing a range of data on grip strength and movement patterns. This data was analyzed for flexion, rotation, and variability across different age and gender groups, providing a basis for assessing individual progress and comparative insights. Results indicated that the device could accurately track rehabilitation metrics, with participants showing measurable improvements in grip strength, flexion, and control across therapy sessions. On average, an 8% increase in flexion and a 12% increase in gyroscopic readings were observed per session. The data revealed distinct performance patterns among participants, supporting the device’s potential to personalize therapy plans and enhance rehabilitation outcomes.