rep-counter

Native body tracking, pose rules, exercise counting and body interaction for React Native and Expo. Camera, inference, tracking and rendering run natively.

Count exercise reps on-device, for free — no SDK key, no cloud. Apple Vision body-pose rep counting with hysteresis + dual-joint anti-cheat. The open alternative to paid pose-fitness SDKs.

An AI-powered personal fitness coach with smart workout planning and progress tracking.

🏋️‍♂️🤖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.

Your gym spotter from a video

Real-time AI gym coach using computer vision and pose estimation to analyze exercise form, count reps, track workouts, and deliver AI-powered voice feedback. Built with Python, MediaPipe, OpenCV, Streamlit, Groq LLaMA, and SQLite.

Premium responsive product showcase for NeuroKinetIQ a real-time AI gym coach using computer vision for pose analysis, rep counting, form correction, voice coaching, and workout tracking.

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.

A computer vision pipeline that detects squats and counts reps in real time from video using MediaPipe pose estimation, geometric joint-angle analysis, and a hysteresis-based state machine — tested across multiple camera angles including front-facing and side profile views.

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

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.

Recognizes 13 exercises and yoga poses and counts reps (squats, push-ups, lunges) in real time from a webcam, using joint angles from MediaPipe pose. No model training needed.

A super-minimal, offline-first rep counter PWA for the gym. Counts your reps to a tempo you set — or tap anywhere. Straight sets, pyramids, EMOM, AMRAP, Tabata, rest-pause and clusters on one black screen. No accounts, no logging, no dependencies.

RepCounter AI: free rep counter that runs in your browser. Record or upload a set; video never leaves your device.

Interval rep trainer PWA: work timer, rest timer, tap-to-count reps. Installs on iPhone and Android, works offline.

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.

AI-powered fitness app built with Flutter — uses on-device pose detection to check your push-up form in real time, counts only correct reps, and ranks you on a leaderboard.

Count exercise reps and cue form from MediaPipe pose keypoints. Squats, lunges, jumping jacks, or your own exercise. Zero dependencies.

Count exercise repetitions from video with pretrained YOLO11-pose + ByteTrack. Hysteresis state machine, near-miss detection, angle chart, web UI and CLI, CPU or GPU, no training.