# FD-YOLO: A YOLO Network Optimized for Fall Detection
## 1 Introduction  
Fall accidents, as defined by the World Health Organization (WHO), occur when an individual unintentionally moves to the ground or a lower level. These incidents are recognized as significant public health issues rather than isolated accidental events. Globally, falls result in over 684,000 fatalities annually, ranking as the second leading cause of unintentional injury-related deaths after road traffic accidents [1]. They account for more than 90% of all hip fractures, with nearly 20% of affected individuals succumbing to related complications within a year [2]. These figures highlight the fatal risks posed by falls, particularly to the elderly, and the substantial medical burden they impose onhealthcare systems. Annually, approximately one-third of individuals aged 65 years and older experience a fall, accounting for more than 50% of hospital admissions for unintentional injuries in this demographic [1; 3]. These data underscore the extensive societal burden. It is anticipated that the incidence of falls will increase alongside the global aging trend. By 2050, the population aged 65 and older is projected to reach approximately 2 billion, constituting approximately 22% of the global population; consequently, injury and mortality rates associated with falls are expected to rise significantly in aging societies, underscoring the pressing need for preventative technological interventions. Moreover, the rise in single-person households has reduced the likelihood of receiving immediate assistance after a fall, particularly among older adults. This delay can lead to the \"long-lie\" phenomenon, where individuals remain immobilized for over an hour, significantly increasing secondary risks such as pressure ulcers, dehydration, hypothermia, and mortality. Thus, there is an escalating demand for accurate fall detection systems capable of facilitating timely interventions [2]. Early fall detection systems predominantly utilized wearable sensors, such as accelerometers and gyroscopes, integrated into personal emergency response systems (PERSs) [4; 5; 6]. These sensors effectively monitored body movements but had critical limitations, including the need for precise placement on the body, which could shift during movement, compromising accuracy. To address these challenges, recent advancements focus on integrating sensor data with artificial intelligence (AI) to improve calibration and analysis [7; 8; 9]. AI offers the potential to significantly improve fall detection accuracy by compensating for sensor data imperfections and learning movement patterns to obtain more reliable fall detection. Current studies aim to enhance the accuracy of sensor-based fall detection through advanced data preprocessing and algorithm optimization while striving to develop cost-effective equipment. Nonetheless, a fundamental limitation of these systems was the need to attach external sensors to the body. Although this method might be effective for high-risk individuals such as those predisposed to frequent falls, it had notable limitations when attempting to detect sudden fall incidents in the general population. Moreover, although sensor-based systems can detect falls, they relied on wireless communication to alert others, which introduced additional challenges [10]. For instance, if the communication network malfunctions or if the user was alone during a fall, timely intervention could not occur. Furthermore, in countries such as South Korea, the transmission of bodily data over the Internet could violate privacy protection laws, complicating the possibility of a rapid response. To solve these problems, researchers have increasingly used smartphones as an alternative. This approach involved transmitting sensor data to a smartphone, where AI processed the information and sent alerts externally if necessary [11]. Although this method addressed some of the communication challenges associated with traditional systems, it did not resolve the issue of physically attaching sensors to the body.