Traditional physiotherapy requires frequent in-person visits, specialized equipment, and continuous professional monitoring, making it costly and inaccessible for remote patients. Incorrect at-home exercise execution can hinder recovery or cause injuries. This paper proposes a comprehensive remote physiotherapy tracking system integrating a wearable IMU-based hardware solution with an end-to-end application. The ESP32 microcontroller transmits motion data to the application, which securely connects patients and doctors while tracking exercises through analytics. Logical constraints validate exercises, anvisual analytics simplify complex data. A 3D humanoid model programmed in Unreal Engine provides real-time motion tracking and form correction. Sensor fusion techniques, including Madgwick and Kalman filtering, mitigate IMU-related inaccuracies. Session reports enable physiotherapists to provide personalized recommendations, improving rehabilitation outcomes. Designed for portability, security, and real-time feedback, this system enhances accessibility, reduces healthcare costs, and ensures safer, more effective physiotherapy for patients, bridging the gap between remote rehabilitation and professional care.