IoT-Based Rehabilitative and Assisstive Glove for Radial Nerve Palsy Patients

Radial Nerve Palsy is a common peripheral nerve injury, occurring in 2% to 17% of cases involving humeral shaft fractures. Conventional rehabilitation methods, such as physiotherapy and splinting, often lack real-time monitoring and personalized engagement, potentially delaying recovery. This study introduces an innovative IoT-based glove system to address these limitations. The glove facilitates rehabilitation by enabling the patient to use their healthy hand to support the affected hand through a control and working unit. The system integrates a microcontroller, MPU-6050 module, servo motors, and sensors to monitor vital signs, including body temperature, heart rate, oxygen saturation, and electromyography (EMG) signals. A wireless module ensures seamless connectivity with Python-based software on a PC, allowing real-time visualization of sensor data, including x-axis and y-axis movements, during therapy. Data is exportable through a WiFi module, enabling further analysis and storage. Preliminary results demonstrate the system's effectiveness in providing real-time feedback and enhancing patient engagement during rehabilitation sessions, ensuring accurate movement tracking and monitoring of vital signs. This novel approach combines hardware and software, offering a comprehensive solution for self-guided therapy and clinical support. Its applications extend to both clinical and home settings, presenting a cost-effective, accessible tool for improving recovery outcomes in patients with RNP. By integrating real-time monitoring and data-driven insights, the IoT-based glove system holds the potential to revolutionize traditional rehabilitation approaches, making recovery more efficient and patient-centered.