نظام ذكي لتتبع وضعيات الجسم وحساب عدد تمارين الضغط

Pose Detection and Real-Time Push-Up Counter

An AI-based fitness tool that uses pose estimation to detect push-ups, count repetitions, and provide real-time feedback on form using a webcam, video, or live feed.

Project Overview

This intelligent system detects human body posture and automatically counts push-up repetitions using pose estimation and computer vision. It identifies key body points like shoulders, elbows, and hips to track motion, ensuring each push-up is correctly performed with real-time visual feedback.

Key Features

Real-Time Push-Up Detection

• ✅ Detects push-ups and counts repetitions live.
• ✅ Uses key body landmarks for pose estimation.
• ✅ Visual feedback with pose skeleton and rep counter.
• ✅ Elbow angle analysis to ensure correct posture.
• ✅ Progress bar for down/up motion guidance.
• ✅ Supports webcam or smartphone video input.
• ✅ Lightweight; runs on CPU or GPU.
• ✅ Easy integration into fitness apps or kiosks.

Technical Components

• 🧍 Pose Estimation Engine – MediaPipe Pose or YOLO Pose to detect body keypoints.
• 📐 Angle Calculation Module – Computes elbow and shoulder angles to identify push-up phases.
• 🔢 Repetition Counter – Tracks full push-up cycles (down + up) and increments count.
• 🎥 Real-Time Video Processing – OpenCV overlays pose skeleton, count, and status.
• 💻 Visualization & Feedback UI – Displays rep count and progress bar.
• ⚡ Performance Optimization – Lightweight code, optional GPU acceleration.

Technology Stack

• 🐍 Programming Language: Python
• 🎥 Computer Vision: OpenCV
• 🧍 Pose Estimation: MediaPipe Pose / YOLO Pose
• 🖥️ Visualization & UI: cvzone or OpenCV overlays

Use Cases

• 🏋️ Personal Fitness Tracking – Hands-free push-up monitoring.
• 🤖 Smart Gym Equipment – Integrate into gyms or kiosks for exercise tracking.
• 🧠 Physical Therapy & Rehab – Monitor patient exercises non-invasively.
• 📊 Sports Analytics – Track athlete form and training progress.

Conclusion

This AI-powered push-up counter combines pose estimation, computer vision, and repetition analysis for accurate, real-time fitness tracking. It eliminates manual counting and wearable sensors, offering a camera-based, non-invasive solution suitable for gyms, rehab, and sports analytics.