A Complex User Activity Recognition Algorithm based on Convolutional Neural Networks

Using sensors to accurately identify the user's activity is helpful to select suitable indoor localization algorithm, thus improving the positioning accuracy. Traditional user activity recognition technology generally needs to artificially analyze and extract the data features, and sometimes it is difficult to accurately excavate the characteristics of complex user's activity. By making use of the ability of automatically extracting feature of raw data with deep learning algorithm, this paper proposes a novel algorithm for complex user activity recognition based on CNN. As a representative neural network of the deep learning, CNN provides an end-to-end learning model with high recognition accuracy. The algorithm utilizes the original data collected by sensors embedded in a smartphone, such as acceleration sensor, gyroscope sensor and magnetic sensor, as input of neural network after 2D image-like conversion. By training and optimizing the various super parameters of the convolution neural network, such as the layer number of the convolution layer and the number of filters, the features of different activities are automatically learned by CNN. At the same time, we select ReLU as activation function to improve the training speed, and finally build a multiple user complex activity recognition model based on convolution neural network. As a comparison, we implements an activity recognition algorithm based on AdaBoost by using the handcrafted features. Extensive experimental results show that our proposed algorithm can accurately identify six kinds of complex behavior modes, such as walking normallywalking sideways, walking backward, crawling on the floor, pushing a pushcart, going up and down the elevator, and the accuracy is up to 99.8%, which is superior to the AdaBoost based activity recognition algorithm