VitalSync — Digital-Twin Elderly Care & Predictive Health Platform
Digital-twin elderly care combining wearable sensor telemetry, 91.8% health-risk prediction, 95.1% fall detection, SHAP explainability, and symbolic safety guardrails.
The situation & problem
Elderly home monitoring currently suffers from disconnected panic buttons, uncalibrated sensor drift, and alarm fatigue. Clinicians and family caregivers lack a continuous predictive digital twin that contextualizes vitals with historical baselines.
The operational constraints
Resource-constrained edge hardware (ESP32 MCU, MAX30100 pulse oximeter, MPU6050 accelerometer/gyroscope, Neo-6M GPS). Telemetry ingestion contracts required 99.4%+ integrity over lossy wireless connections, sub-second emergency dispatch, and verifiable hallucination-free LLM health memory.
Architectural tradeoffs & what was refused
A generic wearable dashboard streaming unvalidated JSON. A consumer voice assistant hallucinating ungrounded medical advice. Uninterpretable black-box alert thresholds.
What I built & shipped
End-to-end digital twin system: ESP32 edge firmware with Kalman filtering; FastAPI ingestion pipeline with strict schema validation; Next.js real-time caregiver portal; 91.8% health-risk prediction engine with SHAP feature attribution; 95.1% accuracy kinematic fall-detection model; conversational memory bot backed by deterministic symbolic safety guardrails.
Architecture & stack
ESP32, C++, Next.js, TypeScript, Tailwind CSS, FastAPI, Python, PostgreSQL, Supabase, Scikit-learn, SHAP, LangChain.
Result & measurable impact
Achieved 91.8% health-risk prediction accuracy, 95.1% fall detection precision, 99.4% telemetry data-ingestion contract integrity, and zero medical hallucination incidents across simulated clinical evaluations.
Next iterations & roadmap
Clinical trial deployment across regional elder-care residency facilities with real-time ambulatory telemetry.
System surfaces & interface captures
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