Virtual Booth

Rehabilitation exercises play a crucial role in patient recovery, but incorrect posture during these activities can reduce effectiveness and increase the risk of injury. Patients often struggle to maintain proper form without continuous guidance, which highlights the need for a supportive system that ensures safe and effective rehabilitation. This project introduces PoseCare, a posture correction system designed to monitor and guide rehabilitation exercises using Artificial Intelligence based Pose Estimation. PoseCare is implemented on a Raspberry Pi with a camera, providing a cost-effective and portable solution suitable for hospitals, clinics, and home environments. The system applies Pose Estimation algorithms to track patient joint positions and compare them against predefined posture benchmarks for three exercise categories: Range of Motion (ROM), Gait Training, and Posture Correction. When deviations are detected, PoseCare delivers immediate feedback through audio-visual alerts for potentially harmful postures. Additionally, integration with the Blynk IoT platform enables remote monitoring and logging of rehabilitation sessions, including repetitions, duration, and posture errors. Initial testing shows that PoseCare can reliably detect posture deviations under proper lighting conditions, offering real-time feedback that improves exercise safety and supports progress tracking. The system also records rehabilitation data, which can be used to personalize therapy plans and monitor patient improvement over time. In conclusion, PoseCare demonstrates how AI and IoT technologies can be applied to rehabilitation care, providing a practical, scalable, and affordable solution that enhances patient safety and recovery outcomes.
Keywords: Pose Estimation, Rehabilation, IoT Monitoring, Posture Correction