skills.rest
Aradotso
AradotsoOFFICIAL
Explore all skills by this author →
Website𝕏@aradotsoGitHub

game-analytics-platform-computer-vision

Orchestrate YOLO, MediaPipe, and Python for real-time exercise tracking.

5|1|Updated May 16, 2026
Search Tags:#computer vision#YOLO#fitness tracking#MediaPipe#fitness platform
One-click install
npx skills add https://github.com/Aradotso/data-skills --skill game-analytics-platform-computer-vision
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: game-analytics-platform-computer-vision
Source: https://github.com/Aradotso/data-skills/tree/main/skills/game-analytics-platform-computer-vision
Command: npx skills add https://github.com/Aradotso/data-skills --skill game-analytics-platform-computer-vision

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires java, python, nodejs, opencv-python, pandas, pyttsx3, ultralytics, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive platform for real-time fitness tracking using computer vision, offering an alternative to traditional fitness monitoring systems.

Core Features & Use Cases

  • Real-time Exercise Tracking: Tracks user movements during various fitness exercises using webcam input.
  • Multi-Modal Tracking: Combines YOLO for object detection, MediaPipe for pose estimation, and Python scripts for workout metrics.
  • Data Management: Orchestration by Spring Boot backend, and Python AI scripts for data analysis and export.
  • User Interface: React + Vite dashboard for game control and visualization.
  • Use Case: A personal trainer or fitness enthusiast can use this Skill to track and improve their workouts with detailed metrics and real-time feedback.

Quick Start

Install the skill and run it. Access the dashboard at http://localhost:8080.

Frequently Asked Questions about game-analytics-platform-computer-vision

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does real-time fitness tracking with computer vision work?▼

Real-time fitness tracking uses webcam input to analyze exercises by combining YOLO for object detection and MediaPipe for pose estimation, processed through Python scripts to calculate workout metrics and provide immediate visual feedback on a dashboard.

How do I set up a computer vision fitness tracking platform?▼

To set up the fitness tracking platform, install the skill and ensure your environment has Java 17, Python 3.10+, and Node.js LTS configured, then run the application and access the visualization dashboard at http://localhost:8080.

Does this fitness tracking platform require specific frameworks like Spring Boot and React?▼

Yes, the fitness tracking platform requires a Spring Boot backend for data orchestration and a React with Vite frontend for the user interface, while Python AI scripts handle the YOLO and MediaPipe data analysis.

Can I use YOLO and MediaPipe together for exercise pose estimation?▼

Yes, you can use YOLO and MediaPipe together for exercise pose estimation; this platform combines YOLO for object detection and MediaPipe for pose estimation within Python scripts to track user movements during various fitness exercises.

What is the best way to track workout metrics without dedicated fitness hardware?▼

Using computer vision frameworks like YOLO and MediaPipe with a standard webcam provides a comprehensive alternative to traditional fitness monitoring systems, offering real-time analysis and detailed workout metrics without specialized hardware.

Related Skills

jbaranskijbaranski

jeff-skill-install-nodejs

Install and configure the latest Node.js LTS version using nvm.

Community
Intermediate
adiytharpansaadiytharpansa

workout-planner

Create structured workout plans and track exercise routines and progress.

Community
Basic
zenobi-uszenobi-us
68

machine-learning-engineer

Deploy scalable ML models with real-time inference, monitoring, and CI/CD pipelines.

Official
Advanced
ThedouglerThedougler

mediapipe-pose-detection

Debug MediaPipe pose detection by adjusting confidence thresholds and validating landmarks.

Community
Intermediate