Mobile Phone Intelligent Recognition of Human Activity Based on Deep Neural Network

Abstract. Because of the rapid development of smartphone sensor technology and the continuous progress of machine learning algorithms, it has become possible to use smartphones for human activity recognition. Sensors such as accelerometers, gyroscopes, and magnetometers built into smartphones are able to collect human motion data in different activities, which provides a rich data source for activity recognition through mobile phones. In this paper, we change the activation function of the hidden layer of the neural network to observe the effect of this variable on the mobile phone to recognize human activities. Taking Rectified Linear Unit (ReLU)function and Sigmoid function as examples, it is found that the neural network using the ReLU function shows higher accuracy (0. 9518 and 0. 9332) and lower loss value in the early stage and middle to late stage of training. In addition, This paper finds that training for 100 epochs takes significantly less time than the model with the Sigmoid activation function (47. 041 seconds vs 179. 022 seconds). The neural network system built by the relu function has faster operation speed and better performance. This study shows that different hidden layer functions have great influence on the quality of the same neural network, and selecting the best hidden layer function will be an important point for the success of the study.