# Research on CNN-BiLSTM Fall Detection Algorithm Based on Improved Attention Mechanism
###### Abstract  
Falls are one of the significant causes of accidental injuries to the elderly. With the rapid growth of the elderly population, fall detection has become a critical issue in the medical and healthcare fields. In this paper, we propose a model based on an improved attention mechanism, CBAM-IAM-CNN-BiLSTM, to detect falls of the elderly accurately and in time. The model includes a convolution layer, bidirectional LSTM layer, sampling layer and dense layer, and incorporates the improved convolutional attention block module (CBAM) into the network structure so that the one-dimensional convolution layer replaces the dense layer to aggregate the information from channels, which allows the model to accurately extract different behavior characteristics. The acceleration and angular velocity data of the human body, collected by wearable sensors, are respectively input into the convolution layer and bidirectional LSTM layer of the model and then classified and identified by softmax after feature fusion. Based on comparison with models such as CNN and CNN-BiLSTM, as well as with different attention mechanisms such as squeeze-and-excitation (SE), efficient channel attention (ECA) and the convolutional block attention module (CBAM), this model improves the accuracy, sensitivity and specificity to varying degrees. The experimental results showed that the accuracy, sensitivity and specificity of the CBAM-IAM-CNN-BiLSTM model proposed in this paper were 97.37%, 97.29% and 99.56%, respectively, which proves that the model has good practicability and strong generalization ability. fall detection; attention mechanism; CBAM; feature fusion; neural network +
Footnote †: journal: Medical  
elderly. According to the relevant report of the World Health Organization (WHO), about 300,000 people worldwide die from falls every year, and more than half of them are elderly people over 60 years old [2]. According to the data from China's disease surveillance system, falls have become the leading cause of injury-related death among people over 65 years old in China [3]. According to the estimation, more than 40 million elderly people in China fall at least once every year [4]. According to the report of the World Health Organization, falls are one of the primary health-related problems that contribute to the disease burden of the elderly in China. After falling, the elderly are prone to serious physical damage, which may not only affect their psychology but also reduce their ability to live independently. It can be seen that falls have become a serious problem threatening the physical and mental health of the elderly, and this has also become an important issue of social concern. Therefore, the research on fall detection has great social significance. In recent years, investigators have completed a lot of work on fall detection algorithms. According to the different devices and detection methods used, fall detection algorithms are mainly divided into methods based on computer vision, scene sensors and wearable devices [5]. The fall detection method based on computer vision can passively obtain human motion information from monitoring equipment and process the acquired video or image to detect if a fall occurs. However, this method infringes upon the privacy of users, and blockage of a large area of the human body easily leads to misjudgment, which affects the accuracy rate [6; 7; 8]. The method based on scene sensors uses the scene sensors installed in the monitored area to collect data, such as pressure, vibration and sound, to determine if a fall occurs. However, these kinds of sensors have disadvantages, such as high configuration costs, high sensitivity to noise information, high susceptibility to the surrounding environment and high false and missing alarm rates; there are also stringent requirements for equipment disposal in different environments. Therefore, they are not suitable for daily living scenes [9; 10; 11; 12; 13; 14; 15; 16].