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 mobi...
['Sylvester Olubolu Orimaye', 'Chen Hui Lee', 'Eddy Cheng Han Ng']
Learning human-mobile nearness with multiple sensors data from steady and non-steady spaces