# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 1 Introduction
The main contributions of this study are as follows:  
* Proposing a real-time fall detection system based on YOLOv11, enhanced with integrated pose estimation and a Convolutional Neural Network (CNN) architecture, achieving higher accuracy and robustness compared to YOLOv7-based approaches;
* Implementing a dual-camera setup that minimizes occlusions and blind spots in realistic, cluttered indoor environments, increasing reliability in challenging scenarios;
* Introducing a rule-based voice assistant module that interacts with the user after a detected fall, confirming his/her condition and reducing false positive alarms by approximately 75%;
* Evaluating the impact of the voice assistant module by comparing system performance with and without voice interaction on a dataset of 200 annotated sequences collected by our development team. To highlight the advantages of YOLOv11 over its predecessor, YOLOv7-W6-Pose, we present a comparative analysis in Table 1[8]. These improvements make YOLOv11 a strong candidate for deployment in real-time applications, where both speed and reliability are paramount. The switch to this new version enables the system to detect falls with greater contextual understanding, which also benefits the voice-assistance response mechanism. As seen in Table 1, YOLOv11 outperforms YOLOv7-W6-Pose by offering higher accuracy, faster inference, and better robustness in challenging conditions. With its hybrid CNN-Transformer architecture and native pose estimation, YOLOv11 simplifies deployment while reducing computational load. Its end-to-end design and improved feature modeling make it a more efficient and reliable choice for real-time detection and pose tasks. At the same time, advancements in voice-assisted technologies offer new possibilities for interaction between monitoring systems and users. Voice-based communication, rule-based keyword matching and AI can be particularly beneficial for elderly individuals who may have limited mobility or cognitive challenges. Integrating fall detection with an intelligent voice response system creates an opportunity for immediate, context-aware interventions, reducing response time and improving overall outcomes. It is important to note that we do not train or test the audio system ourselves; instead, we rely on existing, validated functions already integrated in Python, ensuring both reliability and ease of implementation. This paper is structured as follows: Section 1 defines the main goals and rationale of the study, focusing on how combining human detection with pose estimation can enhance fall detection systems. Section 2 reviews the current landscape in the field by classifying existing fall detection technologies based on AI paradigms, analyzing AI-based monitoring systems, and identifying key limitations of current solutions. Section 3 outlines the technical setup, including data acquisition, preprocessing steps, the proposed fall detection algorithm, and the integrated alert mechanism. Section 5 presents performance metrics and experimental insights, emphasizing the practical relevance of the system in monitoring elderly individuals. The paper concludes with the Section 6, where key outcomes are interpreted, existing limitations are discussed, and potential future improvements are proposed.