# Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism
## 4 Experiment and Result Analysis
### Comparison of Model Results  
The comparison with the CNN, CNN-BiLSTM and the three most widely used attention mechanisms, namely, squeeze-and-excitation (SE), efficient channel attention (ECA) and the convolutional block attention module (CBAM), is made in this paper to verify the effectiveness of this research model and the improved effect. The training accuracy rate is shown in Figure 9.  
The results are shown in Table 4. It can be seen intuitively that the accuracy, sensitivity and specificity of the model in this paper are the highest. Figure 9 shows that the accuracy of the model in this paper rises slowly, and there is no large-scale oscillation phenomenon. After 480 iterations, the accuracy of the model tends to be stable. The attention mechanism can assign different weights to the feature information to obtain more detailed information about the target needing attention, thus suppressing other useless information. Through the analysis of the CNN-BiLSTM model and the models incorporating the SE, ECA and CBAM attention mechanisms, it can be seen that the accuracy of the models has improved to different degrees after the introduction of attention mechanisms, among which the ECA attention mechanism module has brought the greatest improvement of the accuracy of the models (0.66%). Compared with the SE module, the ECA module uses a \(1\times 1\) convolution layer to replace the dense layer directly after the global mean pooling layer. This module avoids dimension reduction and effectively captures cross-channel interaction. SE and ECA pay more attention to the analysis of the channel domain and are limited to consideration of the interaction relationship between the feature map channels. Using the consideration of the channel and the action scope as the starting points, the CBAM introduces two analytical dimensions of spatial attention and channel attention and realizes the sequential attention structure from channel to space.  
Figure 9: Training accuracy of different models.  
In view of the improvement of the ECA module compared with the SE module, one-dimensional convolution instead of the dense layer is introduced in this paper to aggregate the information between channels for the CBAM's channel attention module, so that the model can allocate attention to two dimensions and thus enhance the improvement effect of the attention mechanism on the model's performance. The results of the experiment show that, compared with the CBAM-CNN-BiLSTM network model, the accuracy, sensitivity and specificity of the improved model have increased by 1.19%, 1.97% and 0.35%, respectively, and that, compared with the CNN-BiLSTM model, the accuracy, sensitivity and specificity have improved by 1.58%, 1.78% and 0.26%, respectively.  
The comparison results with the models proposed in other papers are shown in Table 5. The accuracy and specificity of the model in this paper are the highest. The results show that compared with an adapted RNN, all three indicators have been improved to a certain extent and that, compared with the CNN, the sensitivity and specificity have improved by 5.09% and 2.3%, respectively. Compared with the NT-FDS, the accuracy of the model is similar, but the specificity is 5% higher than that of the NT-FDS, and the false-positive rate is low, which does not cause a waste of resources.  
With the CNN parameters remaining constant, we have investigated the model's accuracy change in one, two and three layers of the LSTM in order to observe the influence of the number of LSTM layers on the classification accuracy. The results of the experiment are shown in Table 6. When the number of LSTM layers is two, the accuracy of the model is the highest, and its performance is better than that of one and three layers. Therefore, we finally set the number of LSTM layers to two.