# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 1 Introduction  
The rapid demographic shift toward an aging global population has introduced new challenges for healthcare systems, particularly in ensuring the safety and independence of seniors living alone. The World Health Organization (WHO) expects the number of people aged 60 and older to double by 2050, reaching over 2 billion globally [1]. This demographic transition increases the demand for long-term care solutions that ensure safety, independence, and dignity for elderly individuals in both institutional and home-based settings. One of the most pressing risks for older adults is falling. Falls represent the second leading cause of unintentional injury deaths worldwide [2], with adults over the age of 65 being particularly vulnerable. Beyond the physical injuries, such as fractures or headtrauma, falls often lead to a loss of confidence, increased fear of falling again, and a decline in physical activity, which further accelerates health deterioration and social isolation [2]. Traditional fall detection systems, such as wearable sensors or floor-based sensors, have proven effective. Still, they often require active user compliance or complex installation, which limits their practicality and scalability [3]. In response to these limitations, computer vision and Artificial Intelligence (AI) have emerged as powerful tools for unobtrusive and intelligent monitoring of human activity, enabling real-time fall detection with increased accuracy. However, most current solutions use automated detection and static alert methods, which frequently result in false alarms because of environmental awareness constraints or the inability to determine the user's condition after an incident occurs [4]. The YOLO (You Only Look Once) object detection framework has gained considerable attention due to its real-time performance and high accuracy in detecting complex movements and body postures [5]. The YOLO family of object detection models has undergone continuous development, resulting in notable improvements in both speed and accuracy. This study extends previous research that demonstrated the feasibility of pose-based fall detection using YOLOv7-W6-Pose [6]. We present an AI-based system that combines real-time computer vision with contextual voice interaction to enhance fall detection in elderly monitoring. Our approach adopts the latest YOLOv11 architecture, offering improved object detection accuracy, faster inference, and better performance in challenging environments [7]. The system also integrates a smart voice assistant capable of engaging in real-time dialogue with the user after a fall is detected. This dual-modality design, merging visual analysis with verbal interaction, aims to reduce false positives, ensure user safety, and improve system responsiveness. This approach, integrated into the NeuroPredict platform developed by our team, advances fall detection technology, supporting better care for older adults. The main objectives of the study are as follows:  
* Improve detection accuracy with an upgraded object identification and position estimation model;
* Decrease false positives by including a voice interaction module that confirms the user's condition prior to sending out notifications;
* Create and assess comprehensive system architecture that guarantees responsiveness, scalability, and privacy in diverse home environments. By enabling proactive and context-aware assistance, this approach advances the development of intelligent monitoring solutions that support safe aging in place.