Analysis of Physiotherapy Practices using Deep Learning

Abstract

Abstract: The proposed physiotherapy assessment system, utilizing deep learning, aims to enhance the accuracy and efficiency of assessments. Traditional manual methods used by physiotherapists are often time-consuming and prone to errors, potentially leading to incorrect diagnoses and treatment plans. This system tackles these challenges by employing advanced deep learning algorithms to detect angles and provide personalized audio feedback to patients based on their posture. The system commences by capturing the patient's video with a webcam and extracting frames using OpenCV. These frames are then analyzed through a media pipe library, which identifies key body points. These points are utilized to connect relevant body parts for specific exercises, calculating angles between them. Subsequently, the system evaluates posture correctness and delivers tailored audio feedback, counting reps if correct or providing guidance if incorrect. Each exercise receives unique audio feedback, offering precise guidance to improve posture. Moreover, the system tracks patient progress and displays visual representations of improvement over time. This feature aids patients in monitoring their progress and fosters motivation to adhere to therapy. By leveraging deep learning algorithms and the media pipe library, this system presents a precise, efficient, and economical approach to physiotherapy assessments.