An examination of non-wearable methods for fall detection

Abstract

Amidst the emergence of an aging global population, elderly healthcare has evolved into a pressing societal concern. The World Health Organization reports that approximately 646,000 individuals across the globe succumb to fatal falls annually. Notably, those aged over 60 experience the highest mortality rate, representing the most significant portion of these deaths. Given these stark statistics, there has been a surge of interest in researching fall detection methodologies. This article delves into non-wearable fall detection techniques, emphasizing their various classifications and applications. Initially, we provide an overview of distinct categories within the non-wearable fall detection landscape. Following this, each method is elaborated upon, elucidating its mechanisms and practical applications. To complement this, a brief discourse on prevalent machine learning algorithms employed in fall detection is presented. In culmination, a comparative analysis of each technique is provided, highlighting their respective merits and limitations. The objective is to furnish readers with a holistic perspective on the current state of non-wearable fall detection. As we gaze towards the horizon, it becomes evident that advancing this domain is paramount to safeguarding our elderly and reducing preventable fatalities.