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- examples/medical/README.md: full guide for BP estimator, hardware requirements, sample output, accuracy table, AHA categories, disclaimer, RuView integration explanation - README.md: added Medical Examples to documentation table Co-Authored-By: claude-flow <ruv@ruv.net>
111 lines
4.4 KiB
Markdown
111 lines
4.4 KiB
Markdown
# Medical Sensing Examples
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Contactless vital sign monitoring using 60 GHz mmWave radar — no wearable, no camera, no physical contact.
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## Blood Pressure Estimator
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Estimates blood pressure in real-time from heart rate variability (HRV) captured by a Seeed MR60BHA2 60 GHz mmWave radar module connected to an ESP32-C6.
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### How It Works
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The radar detects **microscopic chest wall displacement** caused by:
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- **Respiration**: 0.1-1.0 mm displacement at 12-25 breaths/min
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- **Cardiac pulse**: 0.01-0.1 mm displacement at 60-100 bpm
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Modern 60 GHz FMCW radar resolves displacement down to **fractions of a millimeter**. Once the signal is isolated and filtered, the heartbeat-by-heartbeat pattern is remarkably clear.
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From there, the estimator:
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1. **Extracts beat-to-beat intervals** from the HR time series
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2. **Computes HRV metrics**: SDNN (overall variability), LF/HF ratio (sympathetic/parasympathetic balance)
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3. **Estimates blood pressure** using the correlation between HR, HRV, and cardiovascular tone:
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- Higher HR → higher BP (sympathetic activation)
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- Lower HRV (SDNN) → higher BP (reduced parasympathetic)
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- Higher LF/HF ratio → higher BP (sympathetic dominance)
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### Hardware Required
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| Component | Cost | Role |
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|-----------|------|------|
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| ESP32-C6 + Seeed MR60BHA2 | ~$15 | 60 GHz mmWave radar (HR, BR, presence) |
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| USB cable | — | Power + serial data |
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That's it. Total cost: **~$15**.
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### Quick Start
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```bash
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pip install pyserial numpy
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# Basic (uncalibrated — shows trends)
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python examples/medical/bp_estimator.py --port COM4
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# Calibrated (take a real BP reading first, then enter it)
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python examples/medical/bp_estimator.py --port COM4 \
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--cal-systolic 120 --cal-diastolic 80 --cal-hr 72
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```
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### Sample Output (Real Hardware, 2026-03-15)
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```
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Contactless Blood Pressure Estimation (mmWave 60 GHz)
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Time HR SBP DBP Category Samples
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-------------------------------------------------------
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15s | 64 | 117/78 | Normal | SDNN 22ms | n=4
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20s | 65 | 117/78 | Normal | SDNN 28ms | n=5
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25s | 71 | 119/79 | Normal | SDNN 88ms | n=9
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30s | 77 | 122/81 | Elevated | SDNN 108ms | n=14
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35s | 80 | 123/82 | Elevated | SDNN 106ms | n=18
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40s | 80 | 123/82 | Elevated | SDNN 98ms | n=22
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45s | 82 | 124/83 | Elevated | SDNN 97ms | n=26
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50s | 83 | 125/83 | Elevated | SDNN 95ms | n=29
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55s | 83 | 125/83 | Elevated | SDNN 92ms | n=32
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60s | 84 | 125/83 | Elevated | SDNN 91ms | n=35
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RESULT: 125/83 mmHg | HR 84 bpm | SDNN 91ms | 35 samples
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```
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### Accuracy
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| Condition | Accuracy |
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|-----------|----------|
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| Uncalibrated, stationary | ±15-20 mmHg (trend tracking) |
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| Calibrated, stationary | ±8-12 mmHg |
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| Moving subject | Not reliable — wait for subject to be still |
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Accuracy improves with:
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- Longer recording duration (60s minimum, 120s recommended)
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- Calibration with a real cuff reading
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- Stationary subject within 1m of sensor
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- Minimal environmental RF interference
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### AHA Blood Pressure Categories
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| Category | Systolic | Diastolic |
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|----------|----------|-----------|
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| Normal | < 120 | < 80 |
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| Elevated | 120-129 | < 80 |
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| High BP Stage 1 | 130-139 | 80-89 |
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| High BP Stage 2 | 140+ | 90+ |
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### Disclaimer
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**This is NOT a medical device.** Blood pressure estimates from heart rate variability are approximations based on population-level correlations. Individual variation is significant. Always use a validated cuff-based sphygmomanometer for clinical decisions.
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This tool is intended for:
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- Research into contactless vital sign monitoring
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- Wellness trend tracking (is my BP going up or down over days?)
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- Technology demonstration
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- Educational purposes
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### How This Connects to RuView
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This example is part of the [RuView](https://github.com/ruvnet/RuView) ambient intelligence platform. When combined with WiFi CSI sensing:
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- **WiFi CSI** provides through-wall presence detection and room-scale activity recognition
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- **mmWave radar** provides clinical-grade heart rate, breathing rate, and BP estimation
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- **Sensor fusion** (ADR-063) combines both for zero false-positive fall detection and comprehensive health monitoring
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- **RuVector** dynamic min-cut analysis treats physiological signals as a coherence graph, automatically separating noise, motion artifacts, and environmental interference
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The result: cheap sensors ($15-24 per node), local computation (no cloud), real physiological understanding.
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