# Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism
## 2 Related Work  
Nowadays, with the improvement of computer computing power, deep learning neural network algorithms are slowly emerging in the field of artificial intelligence (AI). Deep learning, as a machine learning method and also an artificial neural network, can independently construct (train) basic rules according to the sample data during the learning process. With the continuous development of the deep learning model, it becomes increasingly important for inertial sensors to use deep learning to acquire human motion data via signals, analyze and process them and, thus, realize fall detection. Deep learning algorithms can automatically extract the most related features for evaluation, without the need to manually extract predetermined features from the sensor data, and they can provide better outcomes than the traditional machine learning algorithm. At present, the most commonly used deep learning models in common fall detection include the convolutional neural network (CNN), long short-term memory networks (LSTM) and other network-based models. A fall detection model for the elderly was constructed by [PERSON] et al. [20] On the basis of the CNN, which can directly extract critical information from a large amount of tagged data layer by layer through training and optimizing the multilayer convolutional neural network. When we use the CNN model to extract information from data, we usually use a convolution kernel to extract the data's local information, but each piece of local information has a different influence on whether the data can be correctly identified. In recent years, many researchers have integrated an attention mechanism into the convolution module, which proves that the attention mechanism has great potential for improving network performance. [PERSON] et al. proposed the squeeze-excitation (SE) module [21], which can learn the correlation between channels in the feature graph and generate channel attention so that the information-rich channels more greatly concern the network, which brings an obvious performance improvement to the CNN. The convective block attention module (CBAM) [22] is a further extension of the SE module, which pools feature graphs globally according to channels to gain spatial attention. Attention mechanisms have become an increasingly common component of neural architecture and have been widely used in the field of behavior recognition [23; 24; 25]. However, when the CNN is used to extract data features and to detect and categorize the data, the correlation between the time series data will be ignored. In response to this issue, a recurrent neural network is designed to process the time series data. Using LSTM's ability to process the time-order correlation information, we may obtain the behavior pattern that leads to the fall by analyzing the sequence signals before the fall so as to determine whether or not the fall occurs. [PERSON] et al. [26] designed a model architecture based on LSTM that can effectively detect falls and run on wearable devices. [PERSON] et al. [27] used a bidirectional long-term and short-term memory neural network to detect falls. The results of the experiment showed that bidirectional LSTM better balanced the accuracy and detection delay, but the LSTM network had disadvantages in parallel processing and ignored the spatial characteristics of data. In [28], the authors argue that the LSTM cannot distinguish highly similar activities. In [29], the author considers using the BiLSTM instead of the LSTM for forecasting problems in the time series analysis. The final prediction result of the bidirectional LSTM is jointly determined by the forward layer and the reverse layer. Based on the CNN and LSTM, [PERSON] et al. [30] designed a CNN-LSTM model for fall detection that uses the CNN to extract features and then the LSTM layer to obtain a continuous time series. The method can effectively improve the accuracy of the algorithm, and the feasibility of the algorithm has been verified by experiments. [PERSON] et al.