Daily health data accumulated from wearables, sensors, and smart scales in Home Assistant creates a high-value dataset for AI analysis. With sufficient data (minimum 30–90 days), Machine Learning detects patterns invisible to human observation and predicts health events 1–7 days in advance.
Input Data for Health ML Models
Feature vector for health analytics: daily HRV, 24-hour average SpO2, sleep score and stage distribution, activity score (PIR and door sensor counts), weight trend (7-day rolling average), bathroom visit frequency (door sensor), refrigerator open count, bedroom temperature and humidity.
All data resides in the HA History Database (SQLite/PostgreSQL), accessible via the HA Statistics API or Python HA Client Library.
ML Models for Edge Deployment
For Home Assistant on Raspberry Pi 5 or Proxmox Mini PC, practical ML options are: Random Forest Classifier (sklearn) for binary classification (normal/abnormal) — model under 1MB, runs on CPU. Isolation Forest for unsupervised anomaly detection in time-series data — no labelled training data required during early deployment. LSTM (TensorFlow Lite) for sequential pattern recognition in sleep and activity sequences — requires approximately 50MB RAM.
Research-Validated Use Cases
UTI prediction: bathroom visit frequency increasing more than 50% combined with declining sleep score and activity score predicts UTI risk 2–5 days in advance (Oregon OHSU Research, 2021).
Fall prediction: sustained HRV decline over 3 days plus reduced gait speed (measured from walking pace through PIR array) plus declining sleep quality increases fall risk in the following week by 3.2x (JMIR Research, 2022).
Deploying ML in Home Assistant
A Python script in HA Scripts or AppDaemon runs ML inference every 6–12 hours, pulling features from the HA History API and returning prediction results as an HA sensor entity (sensor.health_risk_score, value 0–100).
Automation triggers: Risk Score above 60 → Yellow LINE alert. Risk Score above 80 → Red LINE alert plus emergency contact call. Risk Score declining from above 60 to below 40 for 3 consecutive days → family notified of improvement.
Data Privacy and PDPA
All ML training and inference runs locally on the HA machine — no Protected Health Information leaves to cloud services. Trained models trained on personal data remain on-device, ensuring compliance with Thailand’s PDPA (B.E. 2562) and healthcare data ethics.