# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 1 Introduction  
Global population aging is a demographic phenomenon marked by the increase in the proportion of elderly in the total population. According to the United Nations, the global population of people aged 65 and over is expected to double by 2050, reaching 1.5 billion [1]. As the population ages, the incidence of age-related health problems is increasing. In addition, aging brings with it increased frailty and a greater risk of domestic accidents, among which falls are particularly significant. According to data from the World Health Organization (WHO), there are over 684,000 catastrophic falls globally each year, with most victims being older than 60 [2]. In a world of ever-evolving technology, elderly care has become an area of major interest. One of the most important aspects of this area is the detection of falls, where falls represent a significant risk to the health and well-being of older people [3]. The aging of the population puts pressure on healthcare systems by raising the need for services, making labor shortages worse, and increasing the risk of both acute and chronic illnesses, including injuries from falls. The integration of advanced technologies in elderly care is revolutionizing the industry, bringing significant improvements in their safety, efficiency, and quality of life [4]. By leveraging artificial intelligence and prioritizing user-centered design principles, we can develop more effective and user-friendly solutions [5]. These advancements not only address technical shortcomings but also contribute to a more supportive healthcare ecosystem for aging populations worldwide [6]. Fall detection systems are essential for the elderly, particularly those living independently or with minimal supervision [7]. These systems are designed to detect falls promptly and alert caregivers or emergency services, reducing the time to medical intervention and potentially preventing serious injuries [8]. However, despite advancements in technology, existing fall detection systems still face challenges and limitations that hinder their effectiveness, such as (1) a high rate of false alarms [9], which can lead to user frustration and loss of trust in the system; (2) high costs [10], limiting their accessibility for elderly individuals with low incomes; and (3) real-time detection limitations, delaying necessary interventions [11]. Fall detection technologies can be divided into two main categories: computer vision-based approaches and non-computer vision-based approaches. Non-computer vision-based methods such as accelerometers and vibration sensors detect falls using sound, vibrations, and human body movements. Many people find accelerometers inconvenient to wear, and these sensors are susceptible to background noise, leading to false alarms. To overcome these limitations, computer vision-based fall detection methods are gaining popularity, as they use information from images and videos [7]. When developing a fall detection system for the elderly, the choice of object detection architecture is essential. The system must be able to quickly and accurately detect falls to allow immediate intervention. A brief comparison between wearable technologies and vision-based systems is presented in Table 1.  
Three of the most popular architectures used for object detection are YOLO (You Only Look Once) [12], SSD (Single-Shot MultiBox Detector) [13], and RetinaNet [14]. Each of these architectures offers distinct advantages and disadvantages in terms of speed, accuracy,