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Squat-Coach is an innovative edge AI project that combines MediaPipe pose estimation and the local Gemma model to deliver fully offline real-time squat guidance. Using a teacher-student training paradigm, it generates training data via Gemini and fine-tunes the local model, providing professional fitness feedback while protecting privacy.
Section 01
Squat-Coach is an innovative edge AI project that combines MediaPipe pose estimation and the local Gemma model to deliver fully offline real-time squat guidance. Using a teacher-student training paradigm, it generates training data via Gemini to fine-tune the local model, providing professional fitness feedback while protecting privacy.
Section 02
In the fitness field, correct posture is crucial for training effectiveness and safety, but professional coaches are expensive and cannot be available at all times. Squat-Coach aims to provide an always-accessible, fully private virtual fitness coach using edge AI technology. Its core vision is to create a coach agent with visual input, edge inference, real-time feedback, and intelligent decision-making capabilities—not just a simple overlay of AI text on pose data.
Section 03
Adopting a layered architecture:
Section 04
The core selling point is that all processing is done locally—workout videos never leave the device, and it works even without an internet connection. Dependencies (MediaPipe WASM, JS packages, pose model) are downloaded once via the setup.sh script, with no network requests during runtime, ensuring privacy and reliability.
Section 05
Innovative training method:
Section 06
Technical implementation:
Section 07
Application Value:
Project Status: Core functions (local pose detection/rep counting/angle calculation, etc.) are implemented; Gemma fine-tuning and dataset auditing are ongoing Future Directions: Support more exercise types, personalized training plans, integration with fitness devices.
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