A Comprehensive Review of AI-Driven Fall Detection and Activity Recognition in Smart Elderly Monitoring Systems

Abstract

Elderly fall detection and human activity recognition represent important components of smart healthcare monitoring systems due to the increasing aging population and the growing incidence of fall-related injuries among older adults. Falls frequently result in fractures, mobility impairment, hospitalization, and long-term health complications, thereby increasing the demand for intelligent monitoring technologies capable of continuous supervision and rapid emergency identification. Conventional elderly monitoring approaches primarily rely on wearable sensors, vision-based surveillance systems, inertial sensing devices, and Internet of Things (IoT)-enabled healthcare frameworks for recognizing daily activities and detecting abnormal fall events. Although these systems provide automated monitoring capability, several frameworks experience limitations associated with environmental variation, occlusion, computational complexity, communication instability, privacy concerns, and reduced generalization across real-world healthcare environments. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), hybrid AI, and ensemble learning significantly improved automated activity recognition and fall detection performance through intelligent spatial-temporal feature extraction and multimodal data analysis. This study presents a comprehensive review of recent ML-based, DL-based, hybrid, and ensemble AI-driven frameworks developed for elderly fall detection and activity recognition in smart monitoring environments. The review analyzes wearable, vision-based, IoT-enabled, edge-computing, radar-based, and multimodal monitoring systems along with their datasets, methodologies, performance metrics, and identified limitations. Such AI-integrated monitoring systems possess significant potential to improve patient safety, strengthen continuous healthcare supervision, reduce emergency response delays, and support smart assisted living infrastructures.