## 5 Experimental Results
### Performance Analysis for Multiple Cameras Fall Dataset (MCFD)
#### 5.3.4 Result from Classification Module  
The classification reports in Tables 2 and 3 offer detailed insights into the performance of the classification module, enabling a thorough assessment of the system's accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Sensitivity** & **F1 Score** & **Specificity** \\ \hline Not Fall & 0.91 & 0.94 & 0.92 & 0.87 \\ Fall & 0.91 & 0.87 & 0.88 & 0.91 \\ \hline Accuracy & & 0.91 & \\ \hline \end{tabular}
\end{table}
Table 2: Classification report of all the key points for Multiple Cameras Fall Dataset (MCFD).  
Figure 8: G*Power analysis results illustrating the critical t-distribution and the calculated power for the study.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Sensitivity** & **F1 Score** & **Specificity** \\ \hline Not Fall & 1.00 & 0.96 & 0.98 & 0.98 \\ Fall & 0.94 & 1.00 & 0.97 & 0.96 \\ \hline Accuracy & & & 0.98 & \\ \hline \end{tabular}
\end{table}
Table 3: Classification Report of subset of key points for Multiple Cameras Fall Dataset (MCFD).  
Figure 7: The spectrogram generated by the STFT module for different subjects, highlighting regions with substantial motion.  
We observed that utilizing a subset of key points consisting of the neck, left shoulder, right shoulder, and hip yielded significantly improved results than using all the key points. The subset achieved an impressive accuracy of 98%, outperforming the accuracy of 91% obtained when using all the key points. This finding suggests two things. The first is that the other key points contain a lot of missing data, and the second one is that the selected subset contains key anatomical landmarks that are particularly informative for fall detection. Focusing on these key points could capture essential body movements and orientations strongly indicative of falls.  
However, it is crucial to note that there were some misclassifications within the detection system, particularly in scenarios where falls did not conform to typical patterns, such as when an individual fell onto a sofa instead of the floor. These instances, which often involve softer, more gradual descents that may not generate the distinct motion signatures expected from a fall, were sometimes incorrectly labeled as non-falls. Such nuanced scenarios underscore the complexity of accurately detecting falls across diverse real-world environments. The variability in how falls occur is affected by factors such as the individual's interaction with surrounding furniture and the nature of the fall itself, which presents substantial challenges in automating reliable fall detection.  
The environment in which a fall occurs plays a significant role in detection accuracy. For instance, objects like sofas can cushion a fall, significantly altering the fall's dynamics and the associated sensory data captured by the system. This can obscure critical visual and motion cues necessary for accurate classification. Additionally, the effectiveness of fall detection can be influenced by the camera placement. The current system utilizes eight different cameras positioned around the room to capture multiple angles of potential falls. However, optimal placement and improved sensitivity may enhance the system's ability to capture subtle movements associated with atypical falls, such as those partially obstructed by furniture.