ruvnet--ruview/examples/medical/README.md

Medical Sensing Examples

Contactless vital sign monitoring using 60 GHz mmWave radar — no wearable, no camera, no physical contact.

Blood Pressure Estimator

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.

How It Works

The radar detects microscopic chest wall displacement caused by:

• Respiration: 0.1-1.0 mm displacement at 12-25 breaths/min
• Cardiac pulse: 0.01-0.1 mm displacement at 60-100 bpm

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.

From there, the estimator:

1. Extracts beat-to-beat intervals from the HR time series
2. Computes HRV metrics: SDNN (overall variability), LF/HF ratio (sympathetic/parasympathetic balance)
3. Estimates blood pressure using the correlation between HR, HRV, and cardiovascular tone:
   • Higher HR → higher BP (sympathetic activation)
   • Lower HRV (SDNN) → higher BP (reduced parasympathetic)
   • Higher LF/HF ratio → higher BP (sympathetic dominance)

Hardware Required

Component	Cost	Role
ESP32-C6 + Seeed MR60BHA2	~$15	60 GHz mmWave radar (HR, BR, presence)
USB cable	—	Power + serial data

That's it. Total cost: ~$15.

Quick Start

pip install pyserial numpy

# Basic (uncalibrated — shows trends)
python examples/medical/bp_estimator.py --port COM4

# Calibrated (take a real BP reading first, then enter it)
python examples/medical/bp_estimator.py --port COM4 \
  --cal-systolic 120 --cal-diastolic 80 --cal-hr 72

Sample Output (Real Hardware, 2026-03-15)

  Contactless Blood Pressure Estimation (mmWave 60 GHz)

   Time    HR   SBP   DBP             Category  Samples
  -------------------------------------------------------
   15s |  64 | 117/78 | Normal    | SDNN  22ms | n=4
   20s |  65 | 117/78 | Normal    | SDNN  28ms | n=5
   25s |  71 | 119/79 | Normal    | SDNN  88ms | n=9
   30s |  77 | 122/81 | Elevated  | SDNN 108ms | n=14
   35s |  80 | 123/82 | Elevated  | SDNN 106ms | n=18
   40s |  80 | 123/82 | Elevated  | SDNN  98ms | n=22
   45s |  82 | 124/83 | Elevated  | SDNN  97ms | n=26
   50s |  83 | 125/83 | Elevated  | SDNN  95ms | n=29
   55s |  83 | 125/83 | Elevated  | SDNN  92ms | n=32
   60s |  84 | 125/83 | Elevated  | SDNN  91ms | n=35

  RESULT: 125/83 mmHg | HR 84 bpm | SDNN 91ms | 35 samples

Accuracy

Condition	Accuracy
Uncalibrated, stationary	±15-20 mmHg (trend tracking)
Calibrated, stationary	±8-12 mmHg
Moving subject	Not reliable — wait for subject to be still

Accuracy improves with:

• Longer recording duration (60s minimum, 120s recommended)
• Calibration with a real cuff reading
• Stationary subject within 1m of sensor
• Minimal environmental RF interference

AHA Blood Pressure Categories

Category	Systolic	Diastolic
Normal	< 120	< 80
Elevated	120-129	< 80
High BP Stage 1	130-139	80-89
High BP Stage 2	140+	90+

Disclaimer

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.

This tool is intended for:

• Research into contactless vital sign monitoring
• Wellness trend tracking (is my BP going up or down over days?)
• Technology demonstration
• Educational purposes

How This Connects to RuView

This example is part of the RuView ambient intelligence platform. When combined with WiFi CSI sensing:

• WiFi CSI provides through-wall presence detection and room-scale activity recognition
• mmWave radar provides clinical-grade heart rate, breathing rate, and BP estimation
• Sensor fusion (ADR-063) combines both for zero false-positive fall detection and comprehensive health monitoring
• RuVector dynamic min-cut analysis treats physiological signals as a coherence graph, automatically separating noise, motion artifacts, and environmental interference

The result: cheap sensors ($15-24 per node), local computation (no cloud), real physiological understanding.