Pose Estimation In Physiotherapy Using Machine Learning

Computer Science and Engineering, Machine Learning, Human Pose Detection, Physiotherapy, Media Pipe

Pose detection is an active research area in machine learning, with several real-world applications. Human pose estimation (HPE) is a method of identifying and classifying the joints of the human body. Basically, it's a way to capture a set of coordinates for each joint (arm, head, torso, etc.) known as key points that describe a person's pose. Connections between these points are called pairs. Connections formed between points must be significant. That is, not all points can form pairs. HPE's first goal is to form a skeletal representation of the human body and further process this for task-specific applications. Physical therapy treatments are usually long-term because it takes time to restore a person's movements.To regain movement, you need to do the same exercises every day for several months with the correct posture. Visits to a physiotherapist for each session can be very expensive and not everyone can afford it. must be confirmed. This project is an attempt to create a system that uses computer vision to guide, provide instant feedback, and act as a personal virtual trainer to help people exercise. A system that emphasizes form rather than repetition.In this project we will create an app for the patients and also create a web application for doctors. In web applications, doctors can create a set of exercises for patients and also monitor the exercise of patients. In this project mediapipe technique is used to improve the processing time in the frontend of both patient and doctor apps.

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