## 4 Result  
The data is divided into 80% and 20% for training and testing, respectively. First, we predicted accuracy using the fall detection dataset with the Raw-LSTM model without any pre-processing, such as HOG, as shown in Figure 7a, the Raw-LSTM-Attention model as shown in Figure 7b, the HOG-LSTM model as shown in Figure 7c, and the HOG-LSTM-Attention model as shown in Figure 7d. Finally, we compared the performance of four types of proposed fall detection algorithms with RAW-LSTM, Raw-LSTM-Attention, HOG-LSTM, and HOG-LSTM-Attention. Figure 8a-d show the confusion matrices of the validation data, obtained from the RAW-LSTM, Raw-LSTM-Attention, HOG-LSTM, and HOG-LSTM-Attention architectures for the fall detection system, respectively. It presents four confusion matrices for differ \begin{table}
\begin{tabular}{c c} \hline \hline
**Names** & **Values/Methods** \\ \hline
**Learning rate** & 0.001 \\
**Epochs** & 1000 \\
**Batch Size** & 32 \\
**Optimizer** & Adam \\
**Loss Function** & Crossentropy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters. ent fall detection systems, comparing the actual labels with the predicted labels for five activities: falling, lying, no-activity, sitting, and walking. In Figure 8a, the RAW-LSTM model demonstrates significant limitations in distinguishing between different activities. While it correctly predicts all instances of falling (100% TPs for falling), it severely misclassifies all instances of ADL as falling. This results in a high number of FPs for falls and zero TNs for ADLs, indicating that the model is unable to differentiate between these activities effectively. The model essentially acts as a fall detector that flags any activity as a fall, rendering it impractical for real-world applications due to an unacceptable false alarm rate. In Figure 8b, the Raw-LSTM-Attention architecture demonstrates performance metrics comparable to those of the Raw-LSTM model, suggesting that the incorporation of the attention mechanism does not yield significant improvement. This observation is consistent with the results presented in Figure 8a. In Figure 8c, the HOG-LSTM model demonstrates an improvement in the model's performance. The model correctly predicts 94.5% of falling instances, with a small percentage (5.5%) misclassified as lying. For lying, the model correctly predicts 50% of instances, but it still misclassifies 44.4% as falling and 5.6% as sitting. The model performs exceptionally well in identifying no-activity, with a 100% correct prediction rate. Sitting is correctly predicted 95% of the time, with a small misclassification rate of 5% as lying. Walking is also accurately predicted 97.3% of the time, with a 2.7% misclassified as sitting. Overall, the HOG-LSTM model significantly reduces FPs for falls compared to Raw-LSTM and shows strong performance for distinct ADLs like no-activity, sitting, and walking. Figure 8d shows the best overall performance among the four models. The HOG-LSTM-Attention model correctly predicts 98.2% of falling instances, with only 1.8% misclassified as lying. For lying, the model correctly predicts 61.1% of instances, with 38.9% misclassified as falling. The relatively low classification accuracy for the lying activity, as shown in Figure 8c,d, can be attributed to its visual and motion similarity with the falling activity in radar-based detection systems. Both activities often result in similar horizontal postures and minimal movement, which can confuse the model, especially when relying on Doppler range and scatter plots [9; 10]. The model maintains a high accuracy for no-activity, correctly predicting 97.3% of instances, with a small misclassification rate of 2.7% for sitting. Sitting is accurately predicted 98.2% of the time, with 1.8% misclassified as walking. Walking is also correctly predicted 97.3% of the time, with 2.7% misclassified as sitting. In summary, Figure 8a,b illustrate the model's difficulty in distinguishing falls from four types of ADLs, resulting in a high rate of misclassification.