naganjaneyulu75/NeuroCareIoT: NeuroCareIoT v1.0.0
Abstract
NeuroCareIoT v1.0.0 Initial release of NeuroCareIoT — Reliable Multimodal Edge-Based Alzheimer's Home Monitoring Using NeuroFuseNet. Features SafeFallNet for IMU-based fall detection WanderSenseNet for indoor localization and wandering-risk assessment DailyRoutineNet for temporal routine-deviation detection VitalRhythmNet for physiological anomaly assessment NeuroFuseNet for reliability-aware multimodal fusion Probability calibration using Platt Scaling and Temperature Scaling Predictive uncertainty using Monte-Carlo dropout Uncertainty-gated alerting Context-aware alert policies Modality reliability scoring Personalized resident baselines Drift-aware adaptation Explainable AI support Controlled synchronized multimodal replay ONNX edge-model export support Edge benchmarking utilities Ablation and statistical evaluation support Public Datasets This implementation supports: UP-Fall UJIIndoorLoc CASAS Aruba PPG-DaLiA Edge Deployment The framework supports deployment-oriented experiments using: Raspberry Pi 4B NVIDIA Jetson Nano ONNX Runtime TensorRT-compatible inference Important Note NeuroCareIoT is a research and experimental framework. The public datasets are modality-specific and were not collected as a naturally synchronized Alzheimer's cohort. System-level multimodal evaluation therefore uses controlled synchronized replay. This software is not a certified medical device and is not intended for autonomous clinical or emergency decision-making.
