SmartIoMT：エッジAI生体推論＆スマート医療デバイス基盤

SmartIoMTは、医療モノのインターネット（IoMT）機器向けエッジAIを進化させ、量子化された深層学習モデルを医療用マイコン上で直接実行して、クラウドに依存せず心不整脈、呼吸窮迫、早期敗血症の兆候を検知します。

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)

スマートIoMT、エッジAI生理学的特徴抽出とオンデバイス敗血症・不整脈推論

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

Technical Articles

Related Cluster Architecture Specifications