MILON Pose Analysis Engine

Exercise form analysis and biomechanical assessment using computer vision and pose estimation.

Project Structure

milon-exercise-engine/
├── data/
│   ├── raw/
│   │   ├── videos/          # Raw exercise videos
│   │   └── poses/           # Pre-extracted pose data
│   ├── processed/           # Processed sequences
│   └── annotations/         # Form quality labels, rep counts
├── notebooks/
│   ├── 01_pose_estimation_baseline.ipynb
│   ├── 02_biomechanics_features.ipynb
│   ├── 03_rep_counting.ipynb
│   └── 04_form_scoring.ipynb
├── src/
│   ├── pose/
│   │   ├── estimator.py     # MediaPipe/OpenPose wrapper
│   │   └── tracker.py       # Multi-person tracking
│   ├── biomechanics/
│   │   ├── angles.py        # Joint angle calculations
│   │   └── form_analyzer.py # Form quality assessment
│   ├── exercise/
│   │   ├── squat.py         # Squat-specific analysis
│   │   ├── deadlift.py      # Deadlift analysis
│   │   ├── pushup.py
│   │   └── ...
│   ├── temporal/
│   │   ├── rep_counter.py   # Repetition counting
│   │   ├── pattern_recognition.py  # LSTM classifier
│   │   └── quality_scorer.py       # Rep quality scoring
│   └── utils/
│       ├── video_processor.py
│       ├── visualization.py
│       └── metrics.py
├── configs/
│   └── mediapipe_config.yaml
├── models/
│   ├── pretrained/          # MediaPipe, OpenPose weights
│   └── checkpoints/         # Trained exercise classifiers
├── experiments/
├── tests/
└── requirements.txt

Framework Design

Modular Pipeline Architecture

# Example usage
from src.pose import PoseEstimator
from src.exercise import SquatAnalyzer
from src.temporal import RepCounter, QualityScorer

# 1. Pose Estimation
estimator = PoseEstimator(backend='mediapipe')
poses = [estimator.estimate(frame) for frame in video_frames]

# 2. Exercise-Specific Analysis
analyzer = SquatAnalyzer()
form_feedback = analyzer.analyze(poses)

# 3. Temporal Analysis
counter = RepCounter(exercise_type='squat')
reps = counter.count_reps(angle_sequence)

# 4. Quality Scoring
scorer = QualityScorer()
quality = scorer.score_rep(rep_data)

Pre-trained Models & Datasets

Pose Estimation Models (Pre-trained, Free)

• MediaPipe Pose: Lightweight, real-time, mobile-friendly

  • Download: Automatic via pip install mediapipe
  • Speed: 30-60 FPS on CPU
  • Accuracy: Good for exercise tracking

• OpenPose (Alternative): More accurate, slower

  • Requires: CUDA GPU
  • Download: CMU OpenPose repository

Datasets

Free/Academic:

• NTURGB+D: 56K action sequences (includes exercises)
• Penn Action: 2,326 videos with pose annotations
• LSP (Leeds Sports Pose): 2K sport images

Custom Collection Needed:

• Exercise form videos with expert annotations
• Multi-angle recordings for 3D analysis
• Good/bad form examples for each exercise

Temporal Models (Train from scratch)

• Exercise Classifier: LSTM on pose sequences
• Rep Counter: Peak detection + LSTM validation
• Form Scorer: Rule-based + ML hybrid

Setup

pip install -r requirements.txt

# Download MediaPipe models (automatic)
python -c "import mediapipe as mp; mp.solutions.pose"

Quick Start

# Test pose estimation on video
python src/pose/estimator.py --video data/raw/videos/squat_demo.mp4

# Run squat analysis
python src/exercise/squat.py --video data/raw/videos/squat_demo.mp4

Deployment Considerations

NOT recommended for smartphone app due to:

• High computational requirements (real-time video processing)
• Battery drain from continuous inference
• Camera angle limitations (single viewpoint)

Recommended deployment:

• Desktop/tablet application for home use
• Gym kiosk system with fixed cameras
• Cloud processing with video upload (non-real-time)

Related Repositories

• Nutrition Module: milon-nutrition-training
• Nutrition App: milon-nutrition-app

License

[Your License]