Smart Homes & Aging-in-Place
Passive fall detection and behavior monitoring using UWB radar—enabling safe, independent living.
Pilot Status: Planned | Target: Q1 2027 | Scale: 10–15 Vinhomes units, instrumented with edge confidence monitoring
Fall Detection Without Cameras
Falls are the leading cause of injury in aging populations. Most fall-detection systems are either wearables (many seniors forget/refuse to wear them) or cameras (privacy invasion). Pilot 03 deploys radar-based, privacy-preserving fall detection with self-aware confidence scoring—detecting both macro-movements (falls) and micro-movements (respiratory anomalies) without invading privacy.
Radar-Based Behavioral Sensing
RF Micro-Doppler Signature Classification
Ultra-wideband (UWB) radar sensors running highly optimized, low-power micro-Doppler classification models at the edge:
- ▸Macro-Movement Tracking — fall velocity signatures, gait patterns, lying-down vs. standing posture differentiation
- ▸Micro-Movement Resolution — respiration rate extraction (chest wall micro-motion), heart rate inference via cardio-genic micro-Doppler
- ▸Activity Recognition — walking, sitting, resting, agitated motion differentiation via time-frequency spectral analysis
- ▸No Privacy Invasion — purely kinematic, no imaging, no facial recognition, radar-only architecture
Edge Confidence & False-Alarm Reduction
On-device uncertainty quantification dramatically reduces false alarms while maintaining genuine alert sensitivity:
- ▸Fall Confidence Scoring — multi-channel RF correlation detects genuine falls from accidental radar reflections or pets
- ▸Alarm Severity Tiers — high-confidence falls trigger immediate responder alert; medium-confidence events trigger caregiver notification; low-confidence events logged privately
- ▸Adaptive Learning — baseline respiration/heart-rate models personalized per occupant to reduce behavioral noise variation
- ▸Environmental Resilience — RF signatures calibrated for furniture/fixtures per-room, reducing multi-path artifacts
Micro-Doppler Radar Kinematics
Experience how camera-less fall detection operates at the edge. Select an activity profile below to simulate human movement inside a radar transceiver zone. The engine computes a Short-Time Fourier Transform (STFT) approximation, rendering a live spectral velocity map showing how RF signatures change across different behavioral patterns.
3D Beamforming & Target Localization
Experience sophisticated MIMO radar signal processing: real-time 3D target localization with adaptive beamforming. The antenna array dynamically phases its transmission to focus energy toward targets while rejecting interference. Watch the heatmap reveal signal strength and the confidence ellipsoid show localization uncertainty.
Pilot Cohort & Measurement Plan
- • 10–15 senior residents (65–85 years old)
- • Mixed mobility levels (independent to mobility-limited)
- • Diversified living arrangements (1BR, 2BR, shared spaces)
- • Baseline health screening + ongoing caregiver engagement
- • Fall detection sensitivity & false-alarm rate per occupant
- • Respiration/heart-rate accuracy vs. reference wearables
- • Caregiver response time & engagement metrics
- • Occupant privacy & social acceptance survey data
This pilot draws on V-SENSE's three core engineering frontiers: