Bioliminal

Bioliminal / ML_RandD_Server

Public

Bioliminal ML research & development server

11
0
100% credibility
Found Apr 18, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An AI system that analyzes phone-recorded exercise videos to screen for movement flaws and suggest improvements for injury prevention.

How It Works

1
📱 Discover BioLiminal

You hear about a free app that checks your workout form to help prevent injuries.

2
📥 Get the app

Download the simple phone app and open it up – no complicated setup needed.

3
🎥 Record your movement

Point your camera at yourself doing a bicep curl or squat, and the app tracks your pose automatically.

4
📤 Send for smart check

Tap one button to send your video for analysis – it happens in seconds.

5
📊 See your results

Get back a clear report on your form, spotting any wobbles or imbalances.

6
💡 Learn fixes

Read friendly tips on body connections to improve, like better knee tracking.

🏆 Move better safely

With insights from your reports, you workout confidently and stay injury-free.

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Star Growth

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AI-Generated Review

What is ML_RandD_Server?

ML_RandD_Server is a Python FastAPI server for Bioliminal's R&D in AI-powered movement screening, ingesting pose keypoints from phone cameras to analyze exercises like squats and push-ups for injury risks. Developers upload JSON sessions with MediaPipe BlazePose landmarks via POST /sessions, getting back detailed reports on joint angles, fascial chain issues, rep trends, and fatigue via GET /sessions/{id}/report. It solves the gap in server-side biomechanics analysis for fitness apps, with hooks for future sEMG data and cross-session protocols.

Why is it gaining traction?

Its modular pipeline handles rep segmentation, temporal matching against reference reps using DTW and NCC, and rule-based fascial chain reasoning—all configurable via YAML—without needing custom ML training. The mobile handover package delivers Dart models and schemas for seamless Flutter integration, plus sample fixtures and smoke tests. Early adopters like the research-to-production flow for bioliminal development.

Who should use this?

Biomechanics researchers prototyping pose-based screening tools, fitness app backends needing form analysis beyond basic rep counting, or R&D teams in injury prevention evaluating MediaPipe pipelines. Ideal for Python devs building APIs that flag knee valgus or trunk lean in real-time sessions.

Verdict

At 11 stars and 1.0% credibility, it's raw R&D code—strong docs and tests but light on production polish. Grab it for bioliminal-style research servers if you're okay forking for scale.

(187 words)

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