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Smart Gym - Exercise Tracker

Comprehensive computer vision pipeline for fitness analysis. Identifies people, classifies exercises, counts repetitions, and produces structured analysis. Built with MediaPipe pose estimation, InsightFace face recognition, and scikit-learn classification.

Classifies 22 exercise types with 96.1% cross-validation accuracy (96.9% holdout). Uses peak-valley detection for exercise-independent rep counting.

Key Features
  • 22 exercise classes classified (96.1% CV accuracy)
  • InsightFace (buffalo_l) for person identification
  • MediaPipe PoseLandmarker (multi-person up to 3)
  • Peak-valley rep counting algorithm
  • Label stabilization (EMA + Hysteresis + Min-hold)
  • Annotated output video with skeleton overlay
  • Structured JSON output with rep timestamps
  • Supports: bench press, squat, deadlift, pull-ups, lat pulldown, and 17 more exercises
Demo Video
Project Details
CategorySports & Fitness
Technologies
Python MediaPipe InsightFace scikit-learn OpenCV
StatusCompleted
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