# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### Study Design  
To simulate real-world usage, all tests were conducted in an office room partially adapted to approximate a typical indoor environment. The testing space included common furniture items such as chairs and tables, as well as objects placed on the floor to create cluttered conditions. Although not a fully equipped domestic space, the setup was designed to evaluate the system's ability to operate effectively in constrained and dynamic layouts that resemble home environments where falls are likely to occur.  
After the YOLOv11n-Pose posture classification model detected a potential fall, the system initiated a local voice-based verification process. A speech synthesis module (pyttsx3) vocalized a prompt such as \"Did you fall? Do you need help?\" to assess the user's condition. The system then entered a listening phase using the speech_recognition library, capturing the user's response through the camera's built-in microphone.  
To determine the appropriate response, the system analyzed the transcribed input using two predefined sets of keywords:  
* Help-related expressions (indicating distress or need for assistance): \"I'm not okay,\" \"I need help,\" \"Help,\" \"It hurts,\" \"I fell,\" \"I can't move,\" \"Pain,\" \"Not feeling well\" and \"Need assistance.\"
* Reassuring expressions (indicating safety): \"I'm okay,\" \"I'm fine,\" \"No help needed,\" \"All good,\" \"I'm alright,\" \"No worries\" and \"No problem.\"  
If the system detected any help-related phrases in the speech, it classified the situation as critical and immediately sent an alert via the Telegram Bot API. Conversely, if a reassurance phrase was detected, the event was logged as non-critical and no alert was sent.  
In cases where no valid response was received within a defined timeout (e.g., 15 s), or if the recognized speech confidence was low or unintelligible, the system defaulted to sending an automated alert, assuming that the user might be incapacitated or unable to respond.  
This protocol ensured both prompt verification and minimal intrusion, balancing user independence with effective monitoring. The time delay before triggering the alert, the recognition threshold, and the phrasing of voice prompts were iteratively tested and adjusted during development to optimize user experience and system reliability.  
The study involved multiple test scenarios that aimed to reproduce both fall-related events and normal activities of daily living (ADLs). These included the following:  
* _Simulated falls_: Participants under controlled supervision performed forward, backward, and lateral falls to assess the system's ability to distinguish between different types of genuine falls.
* _Rapid or unstable movements_: Sudden sitting, stumbling, or leaning over (e.g., to pick up objects) were included to evaluate the system's precision in avoiding false positives.
* _Normal behaviors_: Walking, turning, sitting down, standing up, or reaching for objects were performed to ensure the system does not misclassify routine movements as dangerous.  
A total of 200 sequences were recorded using the INSTA360 GO3 cameras, capturing both RGB video and synchronized voice input via the built-in microphone.  
Each fall event was manually annotated to establish ground truth data, and the system's performance was measured in terms of detection accuracy, sensitivity (true positive rate), and specificity (true negative rate). This allowed for quantitative evaluation of the model's ability to differentiate between actual falls and non-fall actions.