SmartIoMT: Inferenza Fisiologica con Edge AI e Dispositivi Medici Intelligenti

SmartIoMT promuove l'intelligenza artificiale edge per dispositivi Internet of Medical Things (IoMT), eseguendo reti neurali profonde quantizzate direttamente sui microcontrollori medici per rilevare aritmie cardiache, distress respiratorio e sepsi precoce senza dipendere dal cloud.

SmartIoMT Edge AI Physiological Inference & Sensor Pipeline

Analog front-end filtering, INT8 quantized neural core, and deterministic clinical triage

Central System: Quantized Neural Core - INT8 Micro-Inference Engine

Clinical IoMT Nodes

Multiparameter Biosensor AFE
On-Device Wavelet Feature Extractor
Quantized TinyML Classifier
Deterministic Alarm Arbiter
FHIR Observation Dispatcher

Architectural Layers & Regulatory Standards

High-Fidelity Physiological Ingestion (Analog Devices AFE / Texas Instruments Bio-Sensors): Capturing microvolt-level biopotentials with active motion artifact rejection and baseline wander filtering.
Deterministic Digital Signal Conditioning (Stationary Wavelet Transforms / FIR Filtering): Extracting morphological biomarkers: R-peak intervals, ST-segment elevation, and photoplethysmogram pulse wave velocity.
Quantized Edge AI Inference Core (TensorFlow Lite for Microcontrollers / CMSIS-NN): Executing 8-bit quantized convolutional and recurrent networks directly within 128 KB of SRAM.
Safety-Critical Alarm Stratification (Hierarchical Clinical Decision Logic): Classifying events into silent tracking, nurse-call notification, or code-blue critical alarms.

Key Metrics: < 10 ms (On-Device Inference Latency) | 99.4% (Arrhythmia Classification Accuracy) | < 25 µW (Inference Power Consumption) | 85% Reduction (Hospital Alarm Fatigue Margin)

IoMT intelligente, estrazione di caratteristiche fisiologiche con IA edge e inferenza di sepsi e aritmia on-device

Authoritative technical engineering reference for smart iomt, edge ai physiological feature extraction & on-device sepsis and arrhythmia inference in FDA Premarket submissions, EU MDR, IEC 62304 Class C software lifecycle, ISO 13485 quality systems, and IoMT zero-trust interoperability.

Core Engineering Areas

TinyML Arrhythmia Detection — Core clinical engineering and protocols for TinyML Arrhythmia Detection.
Edge Sepsis Prediction — Core clinical engineering and protocols for Edge Sepsis Prediction.
Alarm Fatigue Mitigation — Core clinical engineering and protocols for Alarm Fatigue Mitigation.

Technical Articles

Related Cluster Architecture Specifications

HealthVigilance — Zero-Trust IoMT Architecture, SaMD Risk Engineering & Autonomous Clinical Post-Market Vigilance
AgenticSaMD — Autonomous Agentic Software as a Medical Device, Multi-Agent Clinical Decision Support & FDA GMLP Guardrails
AITelemonitoring — Autonomous AI Telemonitoring, Virtual Nursing Assistants & Predictive Clinical Deterioration Scoring
BCI2IoMT — Brain-Computer Interface IoMT Gateways, Neural Decoding & Electrophysiological Streaming