## 5 Discussion  
In this section, we compare our proposed technique for human fall detection to the state of the art in terms of numerous metrics, such as accuracy, sensitivity, specificity, and precision. The comparison in Table 5 provides a comprehensive overview of the performance of various fall detection models. The accuracy of the models ranges from 78.50% (obtained by [PERSON] et al. [80]) to 99.44%. The proposed method has the highest accuracy of 99.44%, which indicates that it has a high percentage of correctly classified fall and non-fall videos compared to other models.  
Sensitivity is an important metric for fall detection, as it measures the ability of the model to correctly identify a fall event. The models have sensitivities ranging from 84.30% (obtained by [PERSON] et al. [87]) to 100.00%. The models by [PERSON] et al. [62] and [PERSON] et al. [90] have perfect sensitivities of 100.00%, indicating that they can classify all fall events correctly. Our proposed method achieved a sensitivity of 99.12%, which is above average in comparison to the other sensitivities of similar works.  
Specificity measures the ability of the model to identify non-fall events correctly. The range of specificities of the compared works is between 64.29% (obtained by [PERSON] et al. [89]) and 98.32%. [PERSON] et al. [87] and [PERSON] et al. [88] also have specificity  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline Proposed & 99.44\% & 99.12\% & 99.12\% & 99.12\% & 99.59\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the proposed method for fall action classification.  
Figure 8: Confusion matrix of the proposed method’s performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Year** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** \\ \hline [PERSON] et al. [87] & 2016 & 79.30\% & 84.30\% & 73.07\% & 79.40\% \\ [PERSON] et al. [74] & 2017 & 97.00\% & 99.00\% & 97.00\% & - \\ [PERSON] et al. [88] & 2017 & 90.90\% & 94.56\% & 81.08\% & 90.84\% \\ [PERSON] et al. [89] & 2018 & 86.21\% & 93.18\% & 64.29\% & 91.11\% \\ [PERSON] et al. [76] & 2019 & 98.43\% & 99.90\% & 98.32\% & - \\ [PERSON] et al. [61] & 2020 & 99.00\% & 99.00\% & - & - \\ [PERSON] et al. [62] & 2021 & 97.23\% & 100.00\% & 97.04\% & - \\ [PERSON] et al. [80] & 2021 & 78.50\% & 98.90\% & - & 99.10\% \\ [PERSON] et al. [90] & 2021 & 93.67\% & 100.00\% & 87.00\% & 83.62\% \\ Our Previous Work [82] & 2022 & 99.03\% & 99.00\% & 99.68\% & 99.00\% \\ Our Proposed Method & 2023 & 99.44\% & 99.12\% & 99.12\% & 99.59\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of our proposed method with other studies on the Le2i dataset.  
less than 85%. The model proposed by [PERSON] et al. [76] demonstrates the highest specificity among the compared works, with a value of 98.32%. The specificity of our proposed method outperformed others, as it achieved 99.12%.  
Precision measures the number of correctly predicted fall events. The models have precision values ranging from 79.40% (obtained by [PERSON] et al. [87]) to 99.59%. The proposed method has the highest precision of 99.59%, indicating that it can accurately predict fall events.  
Overall, our proposed model has the highest accuracy, specificity, precision, and above-average sensitivity compared to other similar methods. These results demonstrate the effectiveness of our approach to human fall detection and its potential for real-world applications.