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npx versuz@latest install freedomintelligence-openclaw-medical-skills-skills-wearable-analysis-agentgit clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.gitcp OpenClaw-Medical-Skills/SKILL.MD ~/.claude/skills/freedomintelligence-openclaw-medical-skills-skills-wearable-analysis-agent/SKILL.md<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA --> --- 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**: ```bash python3 Skills/Consumer_Health/Wearable_Analysis/arrhythmia_detector.py --input apple_health_export.xml --window "last_month" ``` <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->