🏋️‍♂️🤖Real-time AI Gym Trainer using YOLOv8, OpenCV and Python. Features Pose Estimation and Form Correction.

A lightweight, privacy-first, real-time rep counter for common bodyweight exercises. GC_Fit uses MediaPipe's Pose model and OpenCV to detect joint landmarks from a webcam feed and count repetitions of exercises (push-ups, sit-ups, squats) based on simple geometric rules.

Real-time action recognition (squat/pushup/standing) using MediaPipe pose landmarks + joint angle geometry + Random Forest classifier

Exercise Detector is a real-time system that recognizes 7 different exercises using pose estimation and deep learning. It analyzes exercise form, counts repetitions, and achieves 99% accuracy on real-world detection. The lightweight model is optimized for edge devices, enabling fast and efficient performance on mobile and low-end hardware.

RepTrack AI is a computer-vision fitness analytics app that counts exercise reps from uploaded video and live camera input using pose estimation and generalized movement logic.

Deadlift-o-Meter is a project that utilizes a Scikit-Learn model, Mediapipe, and Tkinter to count correct deadlift reps using a live webcam feed. The application analyzes the user's body movements and provides real-time feedback on their performance.