Predicting proximity with ambient mobile sensors for non-invasive health diagnostics
Document Type
Conference Proceeding
Publication Title
2015 IEEE 12th Malaysia International Conference on Communications, MICC 2015
Abstract
Modern smart phones are becoming helpful in the areas of Internet-Of-Things (IoT) and ambient health intelligence. By learning data from several mobile sensors, we detect nearness of the human body to a mobile device in a three-dimensional space with no physical contact with the device for non-invasive health diagnostics. We show that the human body generates wave patterns that interact with other naturally occurring ambient signals that could be measured by mobile sensors, such as, temperature, humidity, magnetic field, acceleration, gravity, and light. This interaction consequentially alters the patterns of the naturally occurring signals, and thus, exhibits characteristics that could be learned to predict the nearness of the human body to a mobile device, hence provide diagnostic information for medical practitioners. Our prediction technique achieved 88.75% accuracy and 88.3% specificity.
First Page
6
Last Page
11
DOI
10.1109/MICC.2015.7725398
Publication Date
10-27-2016
Recommended Citation
Orimaye, Sylvester Olubolu; Leong, Foo Chuan; Lee, Chen Hui; and Ng, Eddy Cheng Han, "Predicting proximity with ambient mobile sensors for non-invasive health diagnostics" (2016). Global Population Health Faculty Publications. 17.
https://doi.org/10.1109/MICC.2015.7725398
https://collections.uhsp.edu/global-population-health_pubs/17