name: wearable-analysis-agent description: Analyzes longitudinal wearable sensor data (heart rate, activity, sleep) to detect anomalies and provide personalized health insights. keywords:

• wearable
• sensor-data
• health-monitoring
• anomaly-detection
• longitudinal-analysis measurable_outcome: Detects atrial fibrillation and sleep anomalies with >90% accuracy using continuous PPG and accelerometer data. license: MIT metadata: author: Biomedical AI Team version: "1.0.0" compatibility:
• system: Python 3.9+ allowed-tools:
• run_shell_command
• read_file

Wearable Analysis Agent

The Wearable Analysis Agent processes data from consumer health devices (Apple Watch, Fitbit, Oura) to monitor vital signs, detect arrhythmias, and analyze lifestyle patterns.

When to Use This Skill

• When analyzing raw export data from wearables (XML, JSON, CSV).
• To detect irregular heart rhythms (AFib) from PPG data.
• For longitudinal sleep quality and circadian rhythm analysis.
• To correlate activity levels with biomarkers or symptom logs.

Core Capabilities

1. Arrhythmia Detection: Algorithms to identify Atrial Fibrillation burdens from irregular tachograms.
2. Sleep Staging: classifying wake/REM/deep sleep from movement and heart rate variability.
3. Activity Recognition: Categorizing physical activities and calculating intensity (METs).
4. Trend Analysis: Detecting significant deviations in resting heart rate or HRV over weeks/months.

Workflow

1. Ingest: Parse standardized health exports (e.g., Apple Health XML).
2. Preprocess: Clean noise, handle missing data, align timestamps.
3. Analyze: Apply specific detection algorithms (e.g., arrhythmia_detector.py).
4. Report: Generate summary of anomalies and trends.

Example Usage

User: "Analyze my Apple Health export for signs of irregular heart rhythm last month."

Agent Action:

python3 Skills/Consumer_Health/Wearable_Analysis/arrhythmia_detector.py --input apple_health_export.xml --window "last_month"