# Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network
## 1 Introduction  
According to forecasts from the United Nations (UN), the world's population is aging. Globally, the proportion of individuals aged 65 and older has risen from 6% in 1990 to 10% in 2024 [1]. This demographic is expected to grow rapidly, with projections indicating that the number of individuals aged 65 and older will reach 2.2 billion by 2080 [2]. People in this age group face challenges in maintaining independent living and are more susceptible to chronic illnesses [3]. Additionally, the number of individuals living alone is on the rise [4, 5]. A white paper published by the Chinese Academy of Social Sciences in 2016 reported that nearly 100 million elderly individuals in China live alone, a number that continues to increase, indicating that over half of the elderly population in China comprises solitary seniors [6]. Known causes of mortality among the elderly include accidental falls, medication noncompliance, and incorrect medication usage [7]. According to the World Health Organization (WHO) data from 2021, falls are the second leading cause of accidental or unintentional injury deaths globally and the primary cause of injury-related fatalities among seniors aged 65 and older in China. The probability of falling and the associated riskof injury or death significantly increase with age. Between 2013 and 2023, the standardized mortality rate for falls among the elderly in China showed a general upward trend [8]. If elderly individuals could issue timely alerts for assistance immediately after a fall, they could receive prompt help, significantly reducing the likelihood of severe injury or death. Consequently, fall detection has emerged as a prominent research focus. Additionally, the WHO reports that there are over 1 billion people with disabilities worldwide [9]. Recent data from the WHO indicates a rising incidence of various diseases, such as diabetes, obesity, and depression, necessitating ongoing monitoring for affected individuals [10]. In recent decades, domestic healthcare expenditures have steadily increased, rising from 4.5% of GDP in 2000 [11] to an average of 7.2% in 2023 [12]. In the coming years, the increasing proportion of elderly individuals in the population, combined with rising per-hour costs in healthcare [13] and investments in related medical technologies, will likely lead to continued growth in healthcare spending. This trend particularly affects individuals of lower socioeconomic status and those with multiple chronic conditions. Furthermore, the aging population and the growing number of patients with chronic illnesses will heighten the demand for healthcare personnel, including registered nurses [14] and physician assistants [15], leading to staffing shortages and increased healthcare burdens [16; 17; 18]. With the rapid development of technology, intelligent companion robots are playing an increasingly important role in our daily lives. Especially in home and healthcare settings, these robots can provide not only basic care and psychological support [19; 20], but also monitor vital signs and the environment to ensure user safety and security [21; 22]. Intelligent companion robots assist the elderly and disabled individuals in overcoming various life challenges, enhancing their quality of life while reducing the burden on family members and caregivers. Consequently, the demand for intelligent companion robots is on the rise. In the development of intelligent companion robots, gesture recognition and fall detection are among the core technologies. Gesture recognition technology enables robots to understand user intentions and respond in real time. Compared to traditional button or touchscreen interactions, gesture recognition offers a more intuitive and natural user experience. Fall detection can monitor users' posture changes to identify potential anomalies, such as falls or loss of balance, which is crucial for ensuring the safety of the elderly and others requiring care.