Непрерывный клинический биосенсинг на ультранизкопотребляющих чипах и гибких платах

WearableIoMT обеспечивает клинический электрофизиологический мониторинг вне стен больницы, сочетая подавление двигательных артефактов и встроенный TinyML для выявления пароксизмальных аритмий в реальном времени.

WearableIoMT Body Area Network Topology

Sub-Microwatt Sensor Fusion and Cryptographic Uplink

Central System: BAN Sensor Hub - Ultra-Low Power SoC

Clinical IoMT Nodes

AFE Photoplethysmography
Multi-Lead Bio-Potential
BLE 5.3 Coded PHY
Secure Element PUF
Local Edge TinyML

Architectural Layers & Regulatory Standards

Analog Front-End (AFE) Sensing (24-bit Delta-Sigma ADC / CMRR > 115 dB): High-impedance bio-potential instrumentation amplifiers with active electrode motion cancelation.
Micro-Power Signal Preprocessing (ARM Cortex-M33 / CMSIS-DSP Vector Kernels): Wavelet filtering and R-peak detection executed on dedicated ultra-low-power DSP co-processors.
Body Area Network Transport (IEEE 802.15.6 / Bluetooth 5.3 Coded PHY): Short-range wireless propagation conforming to medical Body Area Network standards.
Encrypted Telemetry Dispatch (NIST SP 800-38C AES-CCM / ECDH P-256): AES-CCM authenticated encryption pairing sensor patches with patient smartphone gateways.

Key Metrics: < 15 µW (Active Sensor Power Consumption) | 250 Hz (Multi-Lead ECG Sampling Frequency) | AES-CCM (Over-the-Air Encryption Standard) | 14 Days (Continuous Patch Battery Autonomy)

Клиническая электрофизиология на гибких биоклеях

Hospital Holter monitors are bulky and discourage patient compliance. WearableIoMT introduces miniature biocompatible adhesive patches that monitor multi-channel ECG, respiration, temperature, and pulse oximetry for up to two weeks on a single zinc-air battery.

Core Engineering Areas

Sub-Microwatt Analog Front-End Design — Minimizing thermal noise and baseline wander in continuous clinical ambulatory ECG recording.
BLE Coded PHY for Body Area Networks — Overcoming human body tissue attenuation and multipath fading with LE Coded PHY.
TinyML On-Device Arrhythmia Classification — Running convolutional inference directly on wearable microcontrollers to trigger clinical alarms.

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