Architecture - ShanalDivyansh/VisionBiofeedback GitHub Wiki

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.


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.


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.