## 5 Experimental Results
### Comparison with Other State-of-the-Art Methods  
To validate our proposed fall-detection system, we conducted extensive comparisons against several state-of-the-art methods across three distinct datasets: MCFD, URFD, and NTU, as detailed in Table 7. Our system consistently achieved higher sensitivity and specificity across all tested scenarios, demonstrating its robustness and accuracy in detecting fall events.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Sensitivity** & **F1 Score** & **Specificity** \\ \hline Not Fall & 0.97 & 1.00 & 0.99 & 0.97 \\ Fall & 1.00 & 0.97 & 0.99 & 1.00 \\ \hline Accuracy & & & 0.98 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification Report for NTU RGB+D dataset (NTU).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline \hline  & PCANet-SVM [48] & 0.892 & 0.903 & - \\  & HLC-SVM [49] & 0.937 & 0.920 & - \\  & CNN [50] & 0.660 & 0.950 & - \\  & DSM [51] & 0.971 & 0.979 & - \\  & OpenPose-SVM [52] & 0.950 & 0.925 & - \\  & Our proposed method & 1.00 & 0.963 & 0.980 \\ \hline  & GLR-FD [53] & 0.940 & 0.940 & - \\  & Dense-OF-FD [54] & 0.955 & 0.932 & - \\  & Anova-SVM [55] & 0.954 & 0.989 & - \\  & I3D-FC-NN [56] & 0.954 & 0.989 & - \\  & GrassManifold [57] & 1.00 & 0.970 & - \\  & HCAE [58] & 1.00 & 0.930 & - \\  & CNN-LSTM-FD [59] & 0.914 & - & - \\  & OpenPose-SVM [52] & 0.958 & 0.958 & - \\  & Our proposed method & 1.00 & 0.975 & 0.990 \\ \hline  & 2D conv [60] & - & - & 0.917 \\  & LSTM [61] & - & - & 0.961 \\ NTU & 1D conv [62] & - & - & 0.990 \\  & 1D conv [63] & - & - & 0.990 \\  & Our proposed method & 0.980 & 1.00 & 0.980 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of the proposed method and comparison with SOTA.  
In the MCFD dataset, our method outperformed traditional techniques such as PCANet-SVM [48], HLC-SVM [49], and a conventional CNN approach [50]. Moreover, it excelled over the DSM [51] and OpenPose-SVM [52], the latter of which, while not explicitly labeled as'real-time' in its documentation, has been demonstrated to possess characteristics suitable for real-time applications due to its computational efficiency. Our system achieved a perfect sensitivity of 1.00 and a specificity of 0.963, accurately detecting all fall events while maintaining a high True Negative rate.  
For the URFD dataset, our system's performance notably excelled compared to methods like GLR-FD [53], Dense-OF-FD [54], and I3D-FC-NN [56]. We also included newer methods such as Grass\({}_{\text{Manifold}}\)[57] and HCAE [58], which are recognized for their robust performance and potential for real-time processing. Our approach achieved a sensitivity of 1.00 and a specificity of 0.975, proving its efficacy in accurately distinguishing between fall and non-fall events, a critical factor in reducing false alarms and ensuring timely medical responses.  
Lastly, in the NTU dataset, our proposed method showcased an exemplary specificity of 1.00 and a sensitivity of 0.980. This performance underscores our system's ability to handle varied and complex scenarios effectively, proving its suitability for real-world applications. Our comparative analysis highlights our method's state-of-the-art performance and applicability in real-time settings.