State-of-the-art review on fall prediction among older Adults: Exploring edge devices as a promising approach for the future

Abstract

Falling is one of the most serious threats to the health and well-being of older people, resulting in their daily activities and standard of living. In addition, the cost of treating fall-related injuries is substantial, and some patients face incomplete recovery. Current fall prediction methods focus mainly on biological factors such as locomotion, vision, and cognition, often overlooking the multifaceted nature of falls. This paper comprehensively reviewed state-of-the-art fall prediction systems and listed different factors directly associated with falls. We analyzed the current trends and extracted that machine learning, deep learning, sensors, and gait-based fall prediction methods are some of the most prevalent technologies. This paper also identifies the challenges of current fall prediction and prevention systems. It visualizes a road map for future systems that can be integrated into daily life and greatly improve telehealth monitoring and assessment. TinyML-based intelligent wearable technologies have significant potential to predict complex physiological phenomena such as falls. This study highlights the importance of leveraging TinyML-powered smart wearables to aid fall prevention in the geriatric population. By advancing the understanding of existing systems, this research aims to enhance the quality of life for older adults and guide future innovations in the field.