Edge-Intelligent Multimodal IoT Sensor Fusion for Predictive Health Diagnostics in Smart Built Environments: A Low-Power Embedded System with Adaptive Real-Time Alerting

This paper presents HealthSense-Edge, an IoT-embedded smart electronics system for non-invasive predictive health monitoring in indoor environments. The system fuses data from five sensor modalities (PIR motion, CO₂, TVOC, temperature/humidity, acoustic) using a heterogeneous dual-core RISC-V + ARM Cortex-M33 platform. A novel 1D Convolutional-LSTM neural network with 8-bit quantization achieves 96.3% accuracy in detecting respiratory distress, fall risk, and dehydration with only 18 ms inference latency and 230 mW average power consumption. An adaptive behavioral alerting mechanism reduces false alarms by 47% compared to fixed-threshold systems. Validated in a 4-month deployment across 12 smart apartments with 24 elderly residents. All code and data are open-sourced.

IoT, embedded systems, sensor fusion, edge AI, health monitoring, low-power design, RISC-V.

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