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http://dx.doi.org/10.9728/dcs.2017.18.3.559

Human Activity Recognition using Multi-temporal Neural Networks  

Lee, Hyun-Jin (Division of ICT Engineering, Korea Soongsil Cyber University)
Publication Information
Journal of Digital Contents Society / v.18, no.3, 2017 , pp. 559-565 More about this Journal
Abstract
A lot of studies have been conducted to recognize the motion state or behavior of the user using the acceleration sensor built in the smartphone. In this paper, we applied the neural networks to the 3-axis acceleration information of smartphone to study human behavior. There are performance issues in applying time series data to neural networks. We proposed a multi-temporal neural networks which have trained three neural networks with different time windows for feature extraction and uses the output of these neural networks as input to the new neural network. The proposed method showed better performance than other methods like SVM, AdaBoot and IBk classifier for real acceleration data.
Keywords
Activity Recognition; Multi-temporal; Neural Networks; Smartphone;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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