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http://dx.doi.org/10.6109/jkiice.2020.24.9.1224

Detection The Behavior of Smartphone Users using Time-division Feature Fusion Convolutional Neural Network  

Shin, Hyun-Jun (Divsion of Convergence of Computer and Media, Mokwon University)
Kwak, Nae-Jung (Department of Cyber Security, Baejae University)
Song, Teuk-Seob (Divsion of Convergence of Computer and Media, Mokwon University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.9, 2020 , pp. 1224-1230 More about this Journal
Abstract
Since the spread of smart phones, interest in wearable devices has increased and diversified, and is closely related to the lives of users, and has been used as a method for providing personalized services. In this paper, we propose a method to detect the user's behavior by applying information from a 3-axis acceleration sensor and a 3-axis gyro sensor embedded in a smartphone to a convolutional neural network. Human behavior differs according to the size and range of motion, starting and ending time, including the duration of the signal data constituting the motion. Therefore, there is a performance problem for accuracy when applied to a convolutional neural network as it is. Therefore, we proposed a Time-Division Feature Fusion Convolutional Neural Network (TDFFCNN) that learns the characteristics of the sensor data segmented over time. The proposed method outperformed other classifiers such as SVM, IBk, convolutional neural network, and long-term memory circulatory neural network.
Keywords
Deep learning; AI; Time-Series Data Classification; Human activity Recognition;
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