ProactiveGuard: IoT-Driven Fall Detection and RAG-Based Caregiver Assistance for Alzheimer's Patients

Abstract

Alzheimer's disease affects millions of older adults worldwide, with wandering and fall-related injuries ranking among the most preventable yet dangerous consequences of cognitive decline. Current assistive technologies are largely reactive, alerting caregivers only after an incident occurs rather than anticipating the behavioral patterns that precede it. This paper presents a wearable IoT system that addresses this gap through continuous nine-axis inertial sensing, low-power Lo-RaWAN transmission, and a cloud-hosted inference pipeline for real-time fall detection. We evaluate four offline deep learning architectures (LSTM, GRU, 1D-CNN, and Transformer) alongside three online adaptive classifiers on the KFall benchmark dataset comprising 189,987 sliding windows with a 7.56% fall ratio. The stateless LSTM achieves the highest F1-score of 0.8893 with zero missed falls, while the 1D-CNN records the best precision at 0.9507 and the lowest false-alarm count. Beyond detection, the system incorporates a Retrieval-Augmented Generation (RAG) module backed by a Pinecone vector store and BART-Large generation model, enabling caregivers to query patient history and clinical guidance through natural language. All sensor payloads are protected with AES-256 encryption prior to transmission. The results show that pairing non-invasive wearable sensing with sequence models and a conversational caregiver interface produces a practical platform for keeping Alzheimer's patients safer.