# Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism
## 5 Conclusions  
First, the importance of fall detection is discussed in this paper. Then, combined with the application of deep learning in fall detection, the analysis is conducted on the shortcomings of existing fall detection methods. Through the discussion on several of the most widely used deep learning models and attention mechanisms, the CBAM-IAM-CNN-BiLSTM fall detection model is proposed, and the IMU sensor device is used to collect human body movement data to construct a fall data set. For the channel attention module in the CBAM, one-dimensional convolution is used instead of the dense layer to enhance the effect of the attention mechanism on the performance of the model. Combined with the feature fusion method, the accuracy of fall detection is improved by adding contextual \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Layers** & **Accuracy** & **Sensitive** & **Specificity** \\ \hline Fall & 99.74\% & 100\% & 99.69\% \\ Walk & 98.16\% & 97.94\% & 98.19\% \\ Jog & 97.89\% & 86.41\% & 99.70\% \\ Jump & 98.95\% & 95.87\% & 99.53\% \\ Up stairs & 99.11\% & 99.10\% & 99.08\% \\ Down stairs & 99.21\% & 95.54\% & 99.85\% \\ Sit down & 99.87\% & 100\% & 99.85\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CBAM-IAM-CNN-BiLSTM model test results. Figure 10: The test set’s confusion matrix. information. The experimental results show that the accuracy rate of the fall detection model proposed in this paper is 97.37%. Compared with other network models, this model is characterized by higher accuracy, specificity and sensitivity, as well as a stronger generalization ability and better practicability. There are still limitations in this study. [PERSON] et al. [39] argue that real falls are an unpredictable behavior, which makes it difficult to build data sets correlated with real data. Although this study took falls in different situations into account in the collection of data sets and tried to simulate the movement state of the elderly, there are still differences between the simulated data and the real data. Therefore, the high-precision fall detection algorithms implemented in the laboratory are not satisfactory in practice. In addition, the real-time nature of fall detection is particularly important. Compared with the CBAM-CNN-BiLSTM, the method proposed in this study reduces the number of parameters and takes less computation time, and, compared with the CNN and LSTM, the model has higher accuracy but a longer computation time. In the future, we will further improve the data set and add more movements to verify the performance of the model. We will also continue to improve the model, optimize the real-time training process, shorten the training cycle, and reduce misjudgment in future research. Additionally, we will improve the spatial attention module in the CBAM and focus on multiple factors to improve the detection performance of the algorithm, such as changing the basic network. All authors contributed greatly to this article. [PERSON] designed the research protocol, paper framework, and feature fusion algorithm and wrote the manuscript. [PERSON] designed the data collection scheme, model construction and generation of results. [PERSON] was responsible for experimental data collection, data processing and results analysis. [PERSON] proofread the article and revised and commented on the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by the S&T program of Hebei, grant number 203777119D, 19227210D; the Scientific Research Projects of Universities in Hebei Province, grant number ZD2021056; and the National Natural Science Foundation of China's intention-driven network behavior measurement, analysis and system, grant number U20A20180. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this study are available on request from the authors.