# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods  
### Materials  
The system proposed in this study is designed to function as a non-intrusive, real-time fall detection and response solution for elderly care. Its architecture combines computer vision techniques with a lightweight voice assistant, all running locally to ensure privacy, responsiveness, and independence from cloud services.  
To provide reliable video-based monitoring, two INSTA360 GO3 cameras (64 GB, Wi-Fi) were selected for their portability, high resolution (up to 2.7 K), and ability to capture stable footage in dynamic indoor environments. The dual-camera setup ensures increased spatial coverage and reduces blind spots, allowing the system to maintain visibility of the monitored subject from multiple angles. The system processes each video stream independently to enhance robustness against occlusions [6]. To mitigate privacy concerns, the system was designed to operate in real time, processing each video frame on the fly without storing any data locally or in the cloud. Frames are analyzed as they are captured and immediately discarded after processing. Additionally, all data used during development originated from simulated scenarios involving internal participants recruited from within the organization.  
The system uses the YOLOv11 object detection framework, which integrates pose estimation capabilities within a single architecture. This enables simultaneous detection ofthe human body and tracking of critical skeletal keypoints, such as the head, shoulders, hips, knees, and feet, directly from the video feed. YOLOv11 was chosen for its superior inference speed and accuracy in complex visual scenes, particularly those involving movement transitions or unusual body orientations. The model was initialized using pre-trained weights from the official repository on GitHub and further fine-tuned using publicly available datasets relevant to fall detection.  
We used a subset of the publicly available Le2i dataset [53], from which we extracted 130 sequences for evaluation purposes. These included both fall events and daily living activities (e.g., walking, sitting, bending). We manually annotated each Le2i sequence using a custom labeling interface. Labels included binary classification (\"fall\"/\"no fall\") as well as activity tags such as \"bent over,\" \"sitting down,\" \"standing,\" or \"lying down.\" The Le2i subset was particularly useful for evaluating the generalization ability of our model in scenes with different camera angles and environmental conditions.  
Complementing the vision-based detection, the system integrates a voice assistant based on rule-based keyword matching, designed to assess the user's condition immediately after a fall is detected. It uses the speech_recognition library for speech-to-text conversion and pyttsx3 for text-to-speech output. This voice interface was chosen for its lightweight implementation, full on-device local capability (no cloud API dependency), and compatibility with resource-constrained devices. When the system detects a fall, it automatically sends an alert via the Telegram API to a caregiver or family member.  
A GPU-enabled development environment (NVIDIA GeForce RTX 4070, Python 3.10, PyTorch 2.6.0 backend) executes all audio and video processing locally, eliminating the need for internet access and protecting sensitive user data. The system utilizes a local pipeline for both speech-to-text (STT) and text-to-speech (TTS) processing. The device processes all audio directly, using the integrated camera microphone to capture voice input. This approach ensures low latency and preserves user privacy by keeping all data local.