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http://dx.doi.org/10.6109/jkiice.2022.26.8.1165

A Falling Direction Detection Method Using Smartphone Accelerometer and Deep Learning Multiple Layers  

Song, Teuk-Seob (Department of Computer Engineering, Mokwon University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.8, 2022 , pp. 1165-1171 More about this Journal
Abstract
Human behavior recognition using an accelerometer has been applied to various fields. As smartphones have become used commonly, a method for human behavior recognition using the acceleration sensor built into the smartphone is being studied. In the case of the elderly, falling often leads to serious injuries, and falls are one of the major causes of accidents at construction fields. In this article, we proposed recognition method for human falling direction using built-in acceleration sensor and orientation sensor in the smartphone. In the past, it was a common method to use the magnitude of the acceleration vector to recognize human behavior. These days, deep learning has been actively studied and applied to various areas. In this article, we propose a method for recognizing the direction of human falling by applying the deep learning multilayer technique, which has been widely used recently.
Keywords
Smartphone; Acceleration Sensor; Orientation Sensor; Falling;
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