## 1 Introduction  
The global demographic landscape is experiencing a notable shift, with a rapidly growing elderly population [1]. This demographic change also brings an increase in health-related risks among elderly people, most notably a heightened incidence of fall accidents. The World Health Organization reports that around 28-35% of elderly persons suffer a fall annually [2], posing significant risks, especially for those living alone. In such cases, a fall can lead to severe, potentially life-threatening consequences if assistance is not promptly provided. Therefore, in recent years, many systems have been introduced for effective monitoring and rapid response systems to ensure the safety and well-being of the elderly population.  
The development of various monitoring technologies, predominantly focused on fall detection and prevention [3; 4; 5], has been a key response to this challenge. These systems are categorized into wearable device-based, radar-based, and camera-based solutions [6]. While camera-based solutions are popular and easy to install, their implementation raises substantial privacy concerns. With the elderly population valuing their independence and dignity, the intrusive nature of continuous monitoring can be a source of discomfort and stress. Therefore, smart home technologies like camera monitoring systems, increasingly adopted to support aging in place, must balance effective monitoring with privacy protection [7].  
In this context, the preferences of older adults for privacy-protective surveillance methods, particularly those utilizing avatar-based systems, become highly relevant. According to one study [7], older adults naturally have concerns regarding both visual and behavioral privacy, with a stronger inclination towards maintaining visual privacy. A significant majority of healthy older adults show a preference for technologies that transform their actual images into avatars, rather than more invasive methods. These avatar-based systems effectively maintain the functionality of surveillance--essential for safety and security--while addressing the crucial concerns of visual privacy. By replacing real images with avatars, such systems can reduce the feelings of intrusion and discomfort associated with being constantly monitored, thereby aligning with elderly people's expectations for dignity and independence. Such innovative approaches in smart home technologies not only support the practical aspects of aging in place but also prioritize the emotional and psychological well-being of elderly individuals, thus promoting greater acceptance among older adults.  
Building on these challenges, our research presents a novel contribution to the field of elderly care, particularly in addressing the delicate balance between effective monitoring and preserving privacy. First, we introduce an advanced artificial intelligence (AI)-driven camera-based monitoring system based on the YOLOv8 model, employing innovative techniques for real-time subject anonymization. This approach significantly mitigates privacy concerns by replacing traditional invasive video feeds with non-intrusive two-dimensional (2D) avatars. Second, our system incorporates a fall detection algorithm developed using temporal variation in various motion features and a causal convolutional network classifier, ensuring timely responses to emergencies. Our work contributes to the technological and ethical advancements in elderly care and thoughtfully addresses the critical ethical considerations of privacy and dignity. This research represents a significant step towards blending AI technology with a respect for the privacy and dignity of elderly people, emphasizing the importance of safeguarding personal space and autonomy in the development of elderly care systems.