Human Activity Recognition Based On Convolutional Neural Network

Abstract

Smartphones are ubiquitous and becoming increasingly sophisticated, with ever-growing sensing powers. Recent years, more and more applications of activity recognition based on sensors are developed for routine behavior monitoring and helping the users form a healthy habit. In this field, finding an efficient method of recognizing the physical activities (e.g., sitting, walking, jogging, etc) becomes the pivotal, core and urgent issue. In this study, we construct a Convolutional Neural Network (CNN) to identify human activities using the data collected from the three-axis accelerometer integrated in users' smartphones. The daily human activities that are chosen to be recognized include walking, jogging, sitting, standing, upstairs and downstairs. The three-dimensional (3D) raw accelerometer data is directly used as the input for training the CNN without any complex pretreatment. The performance of our CNN-based method for multi human activity recognition showed 91.97% accuracy, which outperformed the Support Vector Machine (SVM) approach of 82.27% trained and tested with six kinds of features extracted from the 3D raw accelerometer data. Therefore, our proposed approach achieved high recognition accuracy with low computational cost.