# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 3 Materials and Methods
### Study Design  
The system was designed to capture video streams from a camera and process these images to detect human poses and identify potential falls. Upon detecting a fall, it sends an alert via a Telegram bot [57] to promptly inform caregivers or family members when a fall is detected, ensuring timely intervention.  
The system begins by capturing video frames from a monitoring setup, as illustrated in Figure 2. These frames are then preprocessed to enhance their quality and suitability for analysis. The next step involves checking for the presence of a person in the preprocessed frames. If no person is detected, the system captures the next video frame. When a person is detected, the system proceeds to the pose estimation phase, applying algorithms to identify key body points and estimate their posture. Here, the system applies pose estimation and runs a fall detection algorithm on the video recordings. The system then evaluates whether a fall has been detected. If no fall is detected, it loops back to continue capturing and processing video frames. If a fall is detected, the workflow transitions to the alert mechanism.  
In the alert mechanism, a Telegram bot is initialized using an access token and prepared to send notifications. Once a fall is detected, an alert message is composed, detailing the event. The message is then sent to the user via the Telegram API, notifying them of the incident.  
To ensure users' privacy is preserved, it is important to note that the information processed by the system is not saved. Video frames are analyzed exclusively in real time, and none of the captured frames or data are stored after processing. This approach prevents unauthorized storage and access to sensitive data while respecting data privacy and security standards.  
Because YOLOv7-W6-Pose recognizes skeletal key points rather than gathering or storing comprehensive visual data about individuals, it allows posture estimation from  
Figure 2: Flowchart of the fall detection system.  
webecam videos in a privacy-conscious way. Since there is insufficient information in the key points to allow for visual identification of an individual, this method effectively anonymizes information. Pose estimation promotes privacy by limiting the acquisition of individually identifying information by concentrating on posture and body movement rather than looks.  
To preserve privacy, anonymized key point data can be safely shared with monitoring systems or caregivers. YOLOv7-W6-Pose is ideal for settings like smart homes or elder care facilities where the goal is to identify significant occurrences (like falls) while maintaining privacy standards because it allows for selective monitoring without continuous video recording.  
Figure 2 underscores the system's capability to provide continuous and reliable monitoring, thereby contributing to improved safety and response times in elderly care scenarios.  
To better understand how this system operates, we will analyze the specific algorithm that underpins its functionality. Our algorithm analyzes a sequence of frames (not just a single static frame). Fall detection relies on tracking the movement of the human skeleton over time by analyzing, for a sequence of human pose skeletons, the position and orientation of key points between consecutive frames to detect abrupt changes that could indicate a fall. This dynamic approach ensures higher accuracy, as falls typically involve a sudden transition from a vertical to a near-horizontal position. This approach provides higher accuracy because a fall usually involves a rapid and sudden transition of the body from a vertical to a near-horizontal position. The detailed workings of this advanced fall detection system are presented in Supplementary Material S1.  
For this system, the output can be single/multiple and is represented by the number of falls for which an alert is sent.