# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 3 Materials and Methods
### System Evaluation Procedure  
As part of the development of an advanced fall identification system, we selected several free videos from online sources [59; 60; 61] for upload and analysis to validate the algorithm's effectiveness. These videos include daily activities such as walking, sitting,  
Figure 3: Real-time fall detection alerts. standing, and bending, as well as different types of falls, including forward, backward, and lateral falls, along with falls from varying heights and with different levels of impact. The results obtained after this preliminary stage were promising, demonstrating the system's ability to detect fall events with high accuracy. Subsequently, we downloaded the Le2i fall dataset [62] and tested the system's accuracy. To extend validation and ensure that the system functions correctly in various scenarios and in real time, videos recorded with a webcam were additionally implemented and tested. These real-time tests allowed the evaluation of the system behavior under dynamic and unpredictable conditions, providing valuable opportunities to adjust and improve the algorithm. The following types of tests were performed:  
1. _Correct detection of persons_--to confirm its ability to accurately detect persons within the frame. This step is essential as it serves as the foundation for identifying subsequent actions, including falls. The real-time object detection capabilities of the YOLO algorithm were utilized to differentiate between persons and other objects in the environment (Figure 4a-c). In cases where no person is detected within the frame, the algorithm can confirm the absence of a person, thereby ensuring the accuracy of the monitoring process (Figure 4d). 2. _Correct identification of falling moments_--to accurately identify the moments when falls occur. This involved analyzing various fall scenarios, such as forward, backward, and sideways falls, and ensuring that the system could correctly classify these events. The goal was to minimize false positives (incorrectly identifying non-fall activities as falls) and false negatives (failing to detect actual falls) (Figure 4e). To evaluate both the false positive and false negative rates, we conducted tests that included a variety of activities of daily living that could be mistaken for falls. For instance, we included scenarios such as changing shoes, picking up objects from the floor, and performing exercises on the ground. 3. _Evaluating model performance in a various lighting condition_--to assess how the model performs under different lighting scenarios, such as very poor or very bright lighting,  
Figure 4: Captured frames from videos in diverse conditions: (**a**) identification of a person; (**b**) identification of a person bending over; (**c**) identification of a person sitting on the chair; (**d**) no person present; (**e**) identification of falling; (**f**) fall detection in environments with very low light levels; (**g**) fall detection in environments with intense lighting conditions. to check the limitations of the model. The algorithm was tested in extreme lighting conditions to assess its limitations:
* _Very Poor Lighting_: The system was tested in environments with very low lighting to evaluate its ability to detect falls accurately in situations where visibility is compromised. Despite the low light levels, the model managed to perform well in detecting falls, maintaining an acceptable level of accuracy. However, there were cases where the visibility was significantly reduced, leading to slight inaccuracies in pose estimation, especially when movements were subtle or fast. Still, the system showed a reasonable tolerance for dim lighting (Figure 4f). * _Very Bright Lighting_: Similarly, tests in very bright lighting conditions, which can lead to overexposure or glare, were conducted to understand how the model handles such challenges. In these cases, the system showed good performance, with the ability to distinguish body movements even in intense lighting.