Advanced Kinetic Activity and Physiotherapy Monitoring System Using CV and Deep Learning

Abstract

The Advanced Kinetic Activity and Physiotherapy Monitoring System offers a cutting-edge approach to exercise and physiotherapy tracking by utilizing Computer Vision (CV) and Deep Learning. Manual observation is frequently used in traditional physiotherapy, which can be subjective and prone to human mistake. In order to increase assessment accuracy, this system provides real-time monitoring, automated tracking of physical activity, concentrating on important metrics including posture, joint angles, and gait patterns. Patients can complete exercises correctly without continual monitoring thanks to the system's ability to analyse live video feeds and Provide feedback on movement change at the end. By integrating the Exercise DB API, the system can anticipate particular workouts and provide comprehensive details about them in response to user input, enabling tailored instruction. User movements are evaluated during the "Predict Exercise" phase, and useful information is offered to enable therapeutic modifications and promote appropriate form. According to preliminary findings, this strategy greatly improves patient outcomes by enhancing the effectiveness and accessibility of physiotherapy and rehabilitation through remote monitoring and customized recommendations.