Purpose of the Project

To create a supportive, remote rehabilitation system tailored for stroke patients, especially those who need help recovering hand movement abilities. Stroke recovery often requires frequent and regular hand exercises, which can be difficult to maintain due to factors like limited access to physical therapy or lack of motivation. Our online rehabilitation tool offers a convenient solution, allowing patients to perform therapeutic exercises from home, with guidance and monitoring from their healthcare providers.

Design and Implementation of a Deep Learning Based Hand Gesture Recognition System for Rehabilitation Internet-Of-Things (RIOT) Environments using MediaPipe

The rehabilitation system uses a camera to detect and track hand gestures in real time as patients perform exercises. The system relies on advanced technologies like MediaPipe and Internet of Things (IoT) to accurately recognize and interpret hand movements. By leveraging these tools, the system can identify specific hand gestures, ensuring patients perform the exercises correctly. Each movement is captured and analyzed, providing immediate feedback to patients on whether they’re executing the gestures as intended. This real-time correction helps patients stay engaged and ensures they follow the correct movements to maximize recovery benefits.

• Help stroke patients build confidence
• Maintain motivation
• Enhance the effectiveness of their recovery process

Our system has undergone rigorous testing in simulated environments to ensure that it operates accurately in real-life conditions.

Factors such as lighting, camera distance, and hand positioning can impact gesture recognition, so extensive adjustments and calibrations have been made to optimize accuracy and consistency. By testing under various conditions, we ensure that patients receive reliable feedback and that the system performs well across different settings, making it a robust tool for home-based rehabilitation.