Customer Success

Exercise Form Correction Using Machine Learning

Discover the future of fitness. Utilizing cutting-edge Machine Learning and Computer Vision, we provide real-time exercise form correction..

Introduction

Explore FitAssist, the innovative platform that merges the potential of Computer Vision, Machine Learning, and Image Processing to revolutionize your fitness journey. With real-time exercise classification and form correction, FitAssist ensures that you exercise safely and effectively while offering personalized feedback and a vibrant fitness community.

Problem Statement

Traditional fitness routines lack real-time feedback on exercise form, leading to a higher risk of injuries and suboptimal results. People often struggle with maintaining proper exercise technique, especially when working out alone. The need for personalized guidance during workouts is crucial, and existing solutions fall short in delivering accurate and immediate assistance.

Challenges

Real-time Analysis: Providing real-time analysis of exercise form required the development of advanced Computer Vision and Machine Learning algorithms capable of processing video footage on the fly.

Precise Pose Estimation: Accurately tracking body joints and movement was challenging, especially when considering different exercise types and body positions.

Video Classification: Automatically recognizing and categorizing exercises needed robust video classification models.

Image Processing: Overcoming suboptimal lighting conditions and crowded workout spaces to ensure accurate data extraction.

Angle Computation: Calculating precise angles and joint positions through computational geometry.

Social Interaction: Creating a community platform for users to share their workouts and receive constructive feedback was a complex endeavor.

"we faced a significant challenge when it came to providing real-time feedback on exercise form. Our athletes trained in various disciplines, and maintaining precise form during workouts was essential to avoid injuries and improve performance. we came across logicbric from a reference, our aim was to somehow get feedback for exercise form factor"

Sports Club Manager

Solution

FitAssist addresses these challenges by combining the latest technologies and techniques:

Computer Vision & Machine Learning: The platform employs cutting-edge Computer Vision and Machine Learning to analyze exercise form in real time.

Learn more about our Artificial Intelligence (AI) & Machine Learning (ML) Services

Our AI & ML dev's have deep insights on how to implement computer vision & ML model for fitness industry

Pose Estimation: Pose Estimation algorithms accurately identify and track body joints, ensuring precise form analysis.

Video Classification: Automated recognition and categorization of exercises provide users with personalized feedback.

Image Processing: Image Processing enhances video quality and ensures accurate data extraction, even in challenging conditions.

Angle Computation: FitAssist calculates crucial angles and joint positions through computational geometry.

Real-time Feedback: Users receive immediate feedback, helping them correct their form and reduce injury risk.

Progress Tracking: A progress tracking feature allows users to monitor their exercise form and performance over time.

Social Interaction: FitAssist's community platform enables users to share their workouts and receive constructive feedback from fellow fitness enthusiasts and professionals.

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Assets
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Technologies Used

Languages: Python, C++, Java (for Android), Swift (for iOS)

Machine Learning and Computer Vision: TensorFlow, OpenCV, PyTorch, PoseNet, MediaPipe

Video Processing: FFmpeg, OpenCV

Image Processing: OpenCV, PIL, SciPy

Video Classification: TensorFlow, Keras

Angle Computation: NumPy, SciPy

Mobile Development: React Native, Flutter (for cross-platform application development)

Backend: Flask, Django REST framework for building API

Database: PostgreSQL, and MongoDB for storing user data, exercise profiles, and feedback

Cloud Computing: AWS, Azure for scalable video processing and analysis

Containerization & Orchestration: Docker, and Kubernetes for environment consistency and scalability

Version Control: Git, GitHub for collaborative development and version tracking

Continuous Integration/Continuous Deployment (CI/CD): Jenkins, Travis CI for continuous integration and continuous delivery

Others: WebRTC for real-time video streaming, Socket.IO for real-time feedback

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