Abstract

Physiotherapy is essential for rehabilitation and the management of physical disabilities, but access to personalized physiotherapy sessions can be limited. This project aims to develop an AI-based physiotherapist trainer using deep learning and computer vision techniques. The system will use a key point annotation system to track and analyze human movement, providing real-time feedback and guidance for physiotherapy exercises. The proposed solution will utilize Python for developing the machine learning model and a suitable frontend framework for building an interactive and user-friendly application. This system will help users perform physiotherapy exercises correctly, reducing the risk of injury and enhancing the effectiveness of rehabilitation.

Introduction

Physiotherapy involves exercises and techniques to help patients recover from injuries, surgeries, or chronic conditions. Proper guidance and feedback are crucial for the effectiveness of physiotherapy exercises. However, many patients do not have regular access to professional physiotherapists, which can lead to improper exercise execution and delayed recovery.

Advancements in AI and computer vision provide an opportunity to develop intelligent systems that can offer real-time feedback and guidance for physiotherapy exercises. By tracking and analyzing human movement through key point annotation, these systems can help ensure that exercises are performed correctly and effectively.

This project aims to develop an AI-based physiotherapist trainer that uses a key point annotation system to monitor and guide users during physiotherapy exercises. The system will be designed to provide real-time feedback, helping users to perform exercises accurately and safely.

Problem Statement

Access to personalized physiotherapy sessions is often limited, leading to improper exercise execution and delayed recovery. There is a need for an intelligent system that can provide real-time feedback and guidance for physiotherapy exercises, helping users perform them correctly and enhancing the effectiveness of rehabilitation.

Aim

The primary aim of this project is to develop an AI-based physiotherapist trainer using a key point annotation system to track and analyze human movement, providing real-time feedback and guidance for physiotherapy exercises.

Objectives

1. To conduct a comprehensive review of existing AI and computer vision techniques for human movement analysis.

2. To collect and preprocess a dataset of physiotherapy exercises with key point annotations.

3. To design and implement a deep learning model for human movement tracking and analysis.

4. To develop a user-friendly application that integrates the deep learning model for real-time exercise guidance.

5. To evaluate the performance of the model using standard metrics and optimize its accuracy and efficiency.

6. To validate the system’s effectiveness through testing with real-world data and feedback from physiotherapists.

Research Methodology

The project will be conducted in the following phases:

1. Literature Review.

   – Review of current AI and computer vision techniques for human movement tracking and analysis.

   – Identification of the most effective methods for key point annotation and exercise guidance.

2. Data Collection and Preprocessing.

   – Collection of a comprehensive dataset of physiotherapy exercises with key point annotations.

   – Preprocessing of data, including normalization, augmentation, and annotation to prepare it for training.

3. Model Design and Implementation.

   – Design of a deep learning model architecture suitable for human movement tracking and analysis.

   – Implementation of the model using Python and deep learning frameworks such as Tensor Flow or PyTorch.

4. Application Development.

   – Development of a user-friendly application using a suitable frontend framework (e.g., Flutter, React Native).

   – Integration of the deep learning model with the application for real-time exercise guidance.

5. Model Training and Evaluation.

   – Training of the deep learning model on the preprocessed dataset.

   – Evaluation of the model’s performance using metrics such as accuracy, precision, recall, and F1-score.

   – Optimization of the model to enhance its accuracy and efficiency.

6. Validation and Testing.

   – Testing the application with real-world data to validate its effectiveness.

   – Gathering feedback from physiotherapists and users to make necessary improvements to the system.

Expected Outcomes

– Development of an AI-based physiotherapist trainer with a key point annotation system for real-time exercise guidance.

– Creation of a comprehensive dataset of physiotherapy exercises with key point annotations.

– Implementation of a deep learning model optimized for human movement tracking and analysis.

– Development of a user-friendly application for real-time physiotherapy exercise guidance.

– Validation of the system’s effectiveness through testing with real-world data and feedback from physiotherapists.

Conclusion

This project aims to revolutionize physiotherapy by leveraging AI and computer vision technologies. By automating the analysis of human movement and providing real-time feedback, the proposed system will help users perform physiotherapy exercises accurately and safely, enhancing the effectiveness of rehabilitation. The successful implementation of this project will contribute to improved physiotherapy outcomes and advance the field of rehabilitation technology.