The aim of the project is to address the challenges faced by patients and healthcare providers in monitoring and assessing fracture rehabilitation progress, while enabling patients to self-monitor their recovery and providing remote monitoring capabilities for healthcare providers
Develop a robust Orthopedic Recovery Monitoring System using the MPU6050 sensor and linear regression machine learning algorithms to objectively measure and track the angle of movement in the hand during the recovery period. Provide accurate predictions and assessments of hand fracture recovery progress based on the collected data, enabling healthcare professionals to make informed decisions regarding patient care and intervention strategies.
In conclusion, the Orthopaedic Recovery Monitoring System offers a novel and effective solution for monitoring and assessing hand fracture recovery. By combining the use of the MPU6050 sensor, Arduino Uno 3 microcontroller, and a machine learning algorithm and a Web App the system provides real-time and objective measurement of hand movement angles, allowing for accurate prediction of recovery levels. The system's remote monitoring capability eliminates the need for frequent clinic visits, providing convenience and reducing costs for patients.