HealinMotion

AI-driven activity recommendation system for Parkinson's disease patients.

What It Does

Reads wearable sensor data in real time, detects freezing-of-gait (FOG) severity, and recommends the right activity for that moment. A MedGemma-4B chatbot explains the recommendation and answers follow-up questions in plain language.

Tech Stack

• Model — Bidirectional LSTM trained on the Parkinson's FOG Prediction dataset
• LLM — MedGemma-4B running on AMD MI300X GPU via ROCm Docker
• Backend — FastAPI + SQLModel + SQLite
• Frontend — React Native (Expo)

Dataset

Parkinson's Freezing of Gait Prediction — clinical wearable accelerometer data with freezing-of-gait annotations. Underrepresented activity classes were balanced using Gaussian perturbation to generate synthetic samples.

Running on AMD Developer Cloud

1. SSH or open Web Console on your droplet

2. Clone the repo

git clone https://github.com/your-repo/AI-Driven-Parkinsons-Actvity-Recommendation.git
cd AI-Driven-Parkinsons-Actvity-Recommendation

3. Start MedGemma-4B on ROCm

docker run -it \
  --device=/dev/kfd \
  --device=/dev/dri \
  --network=host \
  rocm/pytorch:latest \
  python -m vllm.entrypoints.openai.api_server \
    --model google/medgemma-4b \
    --host 0.0.0.0 \
    --port 8000

4. Build and run the backend

docker build -t parkinsons-backend .
docker run --network=host -e VLLM_URL=http://localhost:8000 parkinsons-backend

Backend runs on http://localhost:8080