Learning human-mobile nearness with multiple sensors data from steady and non-steady spaces Online publication date: Thu, 29-Dec-2016
by Sylvester Olubolu Orimaye; Chen Hui Lee; Eddy Cheng Han Ng
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 16, No. 1, 2017
Abstract: As mobile devices are becoming equipped with modern sensors, so is the opportunity to develop more intelligent applications in the areas of internet of-things (IoT) and ambient health intelligence using intelligent data analysis techniques. As such, we present the results of our study on recognising nearness of the human body to a mobile device in a three-dimensional space without having any physical contact with the device. The nearness recognition is done by analysing data from several sensors that are available on a mobile device. We show that the human body generates wave patterns that interact with other naturally occurring ambient signals that could be measured by a mobile device, such as, temperature, humidity, magnetic field, acceleration, gravity, and light. This interaction consequentially alters the patterns of the naturally occurring signals in a steady space to form a non-steady space, and thus, exhibits characteristics that could be learned to predict the nearness of the human body to a mobile device with good accuracy.
Online publication date: Thu, 29-Dec-2016
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