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http://dx.doi.org/10.7838/jsebs.2017.22.2.001

A Machine Learning Approach for Stress Status Identification of Early Childhood by Using Bio-Signals  

Jeon, Yu-Mi (Department of Industrial and Management Engineering, Incheon National University)
Han, Tae Seong (Department of Industrial and Management Engineering, Incheon National University)
Kim, Kwanho (Department of Industrial and Management Engineering, Incheon National University)
Publication Information
The Journal of Society for e-Business Studies / v.22, no.2, 2017 , pp. 1-18 More about this Journal
Abstract
Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.
Keywords
Bio-signals; Machine Learning; Stress Status Identification; Data Mining; Data Analysis;
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Times Cited By KSCI : 6  (Citation Analysis)
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