Predicting proximity with ambient mobile sensors for non-invasive health diagnostics

TL;DR

This paper presents a method using ambient mobile sensors to non-invasively detect human proximity for health diagnostics, achieving high accuracy by analyzing wave interactions with environmental signals.

Contribution

It introduces a novel approach to non-contact health diagnostics by leveraging ambient sensor data to predict human proximity with high accuracy.

Findings

Achieved 88.75% accuracy in proximity prediction

Demonstrated interaction of human body with ambient signals

Provided a non-invasive diagnostic tool

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.