Solution Overview

Our proposed solution, Vision-Biofeedback, is an innovative application designed to revolutionize the exercise experience by integrating real-time biofeedback using DeepLabCut technology.

Application Type:

Vision-Biofeedback is a software application that utilizes DeepLabCut's markerless pose estimation technology to track specific points on the human body during exercise. It operates as a standalone application and has a future possibility to be integrated into existing fitness platforms and applications.

Hosting:

The application for individual users can be installed locally on their computer, providing them with personalized biofeedback during exercise sessions.

Types of Users:

Vision-Biofeedback caters to two primary user groups:

Fitness Enthusiasts: Individuals who are passionate about fitness and seek advanced training techniques to enhance their exercise routines. Physical Therapists: Vision-Biofeedback enables physical therapists to monitor patients' movements in real-time, ensuring they perform exercises correctly and safely during rehabilitation sessions.

Interface:

The application features an intuitive and user-friendly interface designed to provide users with easy access to biofeedback and exercise guidance. Users can view real-time visualizations of their movements, track their progress over time.

Key Features:

Real-time Point Tracking: Utilizing DeepLabCut technology, Vision-Biofeedback offers instantaneous tracking of key points on the body during exercise, ensuring precise movement analysis.

Biofeedback Visualization: The application provides users with visualizations of their biofeedback, helping them understand and improve their exercise form.

Virtual Trainer Integration: Vision-Biofeedback integrates a virtual trainer feature that guides users through exercises.

Technology Stack

DeepLabCut: A state-of-the-art toolbox for markerless pose estimation. Python: Used for backend development, integration with DeepLabCut, and creating the desktop application. OpenCV: OpenCV provides tools for gesture recognition, which you can explore for recognizing specific exercise movements or gestures. GUI Framework (Tkinter): For building the desktop application's graphical user interface.

Solution Diagram

Non-Functional Requirements

Performance: The system should provide real-time feedback without significant delays. Usability: The interface should be user-friendly, allowing easy navigation and interaction. Reliability: The system must be reliable, with minimal downtime and accurate workout tracking. Scalability: It should support an increasing number of users and data volume without degradation in performance. Security: User data, including health and workout information, must be securely stored and transmitted. Interoperability: The ability of the system to work seamlessly with other systems, exchanging and utilizing information between different platforms or services.