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wearable-analysis-agent
Maintainer FreedomIntelligence · Last updated April 1, 2026
Analyze wearable sensor data: activity, sleep, HRV, ECG for health monitoring.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/wearable-analysis-agent
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- 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 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.
Source Doc
Excerpt From SKILL.md
Core Capabilities
- Arrhythmia Detection: Algorithms to identify Atrial Fibrillation burdens from irregular tachograms.
- Sleep Staging: classifying wake/REM/deep sleep from movement and heart rate variability.
- Activity Recognition: Categorizing physical activities and calculating intensity (METs).
- Trend Analysis: Detecting significant deviations in resting heart rate or HRV over weeks/months.
Workflow
- Ingest: Parse standardized health exports (e.g., Apple Health XML).
- Preprocess: Clean noise, handle missing data, align timestamps.
- Analyze: Apply specific detection algorithms (e.g.,
arrhythmia_detector.py). - 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:
Use cases
- Use wearable-analysis-agent for clinical, translational, or medical research tasks.
- Apply wearable-analysis-agent when healthcare-specific guidance is required.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
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