AI-Driven Personalized Fall Prevention for Older Adults

Abstract

Falls among older adults pose a significant public health challenge, impacting quality of life and healthcare costs. This research proposal aims to develop an innovative AI-driven personalized fall prevention system for older adults, leveraging advanced machine learning techniques in computer vision, natural language processing, and reinforcement learning. The proposed system will encompass five key components: (1) Advanced pose estimation and activity recognition using HRNet with attention mechanisms and hybrid LSTM-GCN models; (2) Personalized risk assessment through multi-modal deep learning, combining CNNs, RNNs, and federated learning for privacy-preserving distributed training; (3) Adaptive intervention strategies employing Deep Q-Networks and model-based reinforcement learning with GAN-simulated environments; (4) Human-AI interaction utilizing SHAP values for explainable AI and fine-tuned GPT-3 for natural language communication; and (5) Privacy-preserving techniques including differential privacy and homomorphic encryption. The research will be conducted over a five-year period, involving data collection, model development, large-scale testing, and clinical trials. Expected outcomes include a scalable, privacy-preserving AI system capable of significantly reducing fall incidents among older adults, thereby improving quality of life and reducing healthcare costs. This interdisciplinary research contributes to advancing AI techniques in real-world healthcare applications while addressing critical ethical and privacy concerns, potentially transforming elderly care on a global scale.