SmartIoMT: Edge AI Physiological Inference & Smart Medical Devices

SmartIoMT advances edge artificial intelligence for Internet of Medical Things devices, running quantized deep neural networks directly on medical microcontrollers to detect cardiac arrhythmias, respiratory distress, and early sepsis indicators without cloud dependency.

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)

Smart IoMT, Edge AI Physiological Feature Extraction & On-Device Sepsis and Arrhythmia Inference

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