A Systematic Review and Taxonomy of Fall Detection in Geriatric Care: Current State, Challenges, and Applications
This systematic review provides a critical examination of fall detection (FD) technologies in geriatric care, analyzing both technological innovations and implementation challenges across diverse healthcare environments. The study employs a comprehensive evaluation framework to assess various technological approaches, which consists of wearable sensors and Internet of Things (IoT)‐enabled devices to machine learning (ML) algorithms and video monitoring systems, thereby identifying significant gaps in current research and practice. Despite the demonstrated improvements in detection accuracy and real‐time monitoring capabilities of contemporary systems, persistent challenges emerge in areas of system longevity, user acceptance, and healthcare system integration. Particularly concerning are the ethical implications surrounding privacy preservation and algorithmic transparency in AI‐driven systems, combined with inadequate consideration of demographic variations in fall patterns across different user populations. The analysis reveals an urgent need for developing more personalized, interpretable, and user‐centric FD solutions that address these multifaceted challenges. This work provides evidence‐based recommendations for future research, with particular emphasis on developing robust, ethically sound systems that integrate seamlessly with existing healthcare workflows. The research contributions advance the field through actionable insights for developing more effective and inclusive FD technologies that enhance the safety and independence of elderly individuals without compromising their privacy and autonomy. This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Machine Learning