# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 4 System Implementation  
### System Architecture  
The proposed fall detection system is built on a modular architecture that integrates real-time video analysis, pose estimation, voice interaction, and automated alert functional  
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Library/Tool** & **Purpose** \\ \hline Text-to-Speech & pyttsx3 & Voice prompts (offline, OS-independent) \\ \hline Speech Recognition & speech\_recognition & Captures and transcribes microphone input \\ \hline Keyword Matching & Rule-based keyword filter & Classifies answers into actionable categories \\ \hline Notifications & Webhook Integration & Sends alerts to caregiver(s) \\ \hline \end{tabular}
\end{table}
Table 6: Technical stack used for voice-based interaction and notification.  
\begin{table}
\begin{tabular}{c c c} \hline
**Scenario** & **Description** & **Goal** \\ \hline Forward fall & Simulated tripping & Detect rapid vertical descent \\ \hline Backward fall & Loss of balance while & Detect transition to supine \\  & stepping & pose \\ \hline Side fall & Slip simulation & Detect lateral instability \\ \hline Abrupt sitting & Sudden seated movement & Avoid false positives \\ \hline Bending/picking & \multirow{2}{*}{Leaning down} & Discriminate bending vs. \\ objects & & falling \\ \hline Walking and idle & Normal daily activity & Validate system stability \\ \hline \end{tabular}
\end{table}
Table 7: Description of experimental scenarios and corresponding detection objectives.  
ities. The components are organized into a pipeline that ensures efficient and low-latency response. The main modules include the following:  
* _Video Acquisition Module_: Utilizes two cameras strategically positioned to maximize spatial coverage and reduce blind spots.
* _Pose Estimation and Fall Detection Engine_: Powered by YOLOv11 with integrated pose estimation to extract 17 anatomical keypoints per person.
* _Voice Assistant Interface_: Activates when a fall is detected. It uses Text-to-Speech (TTS) and Speech-to-Text (STT) components to communicate with the user and process verbal responses.
* _Intent Recognition Unit_: Analyzes user responses and categorizes them into actionable intents (e.g., \"I'm fine,\" \"Help,\" or no response).
* _Alert and Notification System_: Sends real-time alerts to caregivers or family members via the Telegram API when assistance is needed.  
Fall detection is triggered by the first video stream that confirms an incident, without waiting for cross-validation from the second camera. This allows for quicker interventions in time-critical scenarios when an emergency alert is automatically sent via the Telegram API to the designated caregivers.  
This design guarantees robustness and fault tolerance. If one camera fails to detect a fall due to occlusion or other factors, the second camera can still independently trigger the appropriate response.  
Figure 4 illustrates the end-to-end workflow of the proposed fall detection system, from video acquisition to emergency alerting. The flowchart highlights the key processing stages, including YOLOv11-based pose estimation, voice-based condition verification, and automated Telegram notifications.