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
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Technologies and Applications (IJISTA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.If you still need assistance, please email firstname.lastname@example.org