What They Do

The client operates in the fitness and wellness technology space, exploring AI-driven solutions to improve exercise accuracy, reduce injury risk, and increase user engagement through real-time feedback. Their objective was to validate whether computer vision and AI could reliably analyze human movement using a live camera feed without requiring specialized hardware.

Services Delivered

AI Proof of Concept (POC) Development

• AI & Computer Vision Architecture
• Exercise Detection & Pose Estimation
• Real-Time Motion Analysis
• AR-Driven Feedback Logic
• Python-Based Model Development
• POC Validation & Performance Testing

Technology Stack:
MediaPipe · OpenCV · TensorFlow · Python

The Challenge

Accurate Exercise Detection and Form Validation in Real Time

Traditional fitness apps rely on manual input or wearable sensors, offering limited insight into exercise form and posture. This often leads to:

• Incorrect exercise execution
• Increased risk of injury
• Poor user engagement due to a lack of feedback
• No reliable way to track repetitions and sets accurately

The client wanted to explore whether an AI-powered, camera-based system could:

• Detect exercises in real time
• Validate posture and movement consistency
• Track repetitions and sets accurately
• Provide immediate corrective feedback

All of this needed to work using a standard camera, making the solution accessible and scalable.

The NonStop Approach

•   AI-First, Computer Vision–Driven POC Design

NonStop approached this engagement as a technical feasibility and intelligence validation exercise, focusing on accuracy, performance, and real-world usability.

• Real-Time Pose Detection & Skeletal Tracking

Using MediaPipe and OpenCV, we implemented real-time pose estimation to track key skeletal landmarks from a live video feed. This enabled continuous monitoring of joint positions, angles, and movement trajectories during exercise execution.

• AI-Based Movement Analysis

TensorFlow models were used to analyze skeletal movement patterns and detect specific exercises. By evaluating motion consistency and joint alignment, the system could differentiate between correct and incorrect form.

• Exercise Form Consistency & Correction

The POC introduced logic to identify posture deviations during exercises. When improper form was detected, the system flagged inconsistencies, laying the foundation for real-time corrective feedback in future iterations.

•   Intelligent Repetition & Set Tracking

By analyzing movement cycles, the system automatically tracked:

• Exercise repetitions
• Sets performed
• Session-level activity

This eliminated the need for manual input while improving tracking accuracy.

Key Features Delivered

• Live Exercise Detection: Real-time recognition of exercises using a camera feed
• Exercise Form Validation: Detection of posture and movement inconsistencies
• Repetition & Set Tracking: Automated counting based on movement cycles
• Progress Monitoring: Exercise logs to track daily activity and streaks
• AR-Ready Feedback Logic: Foundation for visual and contextual feedback overlays

The Impact

Validating AI-Driven Fitness Intelligence

The POC successfully demonstrated that AI and computer vision can accurately analyze exercise movements in real time, using only a standard camera.

The solution enabled the client to:

• Validate the feasibility of camera-based exercise tracking
• Reduce reliance on wearables or manual inputs
• Improve exercise accuracy and posture awareness
• Lay the groundwork for injury-prevention–focused fitness applications
• Increase engagement through real-time feedback and progress tracking

Why This POC Matters & Why NonStop

• Demonstrated the practical, real-world application of AI and AR in fitness scenarios
• Validated real-time pose estimation using a live camera feed for consumer use
• Established a scalable foundation for future AI- and AR-driven fitness products
• Showcased NonStop’s strength in applied AI, computer vision, and real-time system design
• Proven ability to rapidly build POCs and translate complex AI models into usable product experiences

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