SmartIoMT: 端侧 AI 生理特征抽取与高可靠智能医疗终端

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)