Detection of Social Interaction Using Mobile Phones via Device Free Passive Localisation

Detection of Social Interaction Using Mobile Phones via Device Free Passive Localisation

Timothy Dougan (Ulster University, Londonderry, UK) and Kevin Curran (Ulster University, Londonderry, UK)
Copyright: © 2014 |Pages: 16
DOI: 10.4018/IJHCR.2014100102
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Abstract

Mobile devices which make use of 802.11 Wi-Fi are ubiquitous in modern society. At the same time, there is an unmet need in research and monitoring applications, and particularly in those relating to service and healthcare scenarios, to accurately detect the occurrence and hence frequency and duration of human interaction between subjects. Various sensor modalities exist that are able to perform localization of human subjects with useful degrees of accuracy, but in all cases they are either expensive, inflexible, or prone to influencing subject behaviour via the Hawthorne or observer effect. Given the ubiquity of mobile devices, it is the contention of this paper that a system which localizes human presence based on the human body's obstructive effects on RF transmissions through interpretation of perturbation of the Received Signal Strength values generated during transmission, may offer a system that is both inexpensive and flexible, while avoiding the need for direct subject participation, and thus reducing the impact of the Hawthorne effect.
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1. Introduction

The ability to track human motion or detect when a person is present in an area is valuable in a variety of circumstances. Besides surveillance and security applications, for which the utility is obvious, there are multiple service and care scenarios in which these functions are also of use. The IR sensor on an automatic door would be a simple example of the former, while a tracking bracelet worn by a dementia patient is the same to the latter. Research on human behaviour, focussed on capturing the normal activity of human subjects, also benefits from these technologies, in many contexts and via diverse monitoring strategies. In each of these three cases the requirement is to obtain information on human activity at the necessary level of accuracy, while minimising intrusion and cost. Costing must cover both equipment purchase and installation time/fees, and is minimised for obvious reasons. Intrusiveness is more subtle, as it can have an indirect impact on accuracy or ease of use. The Hawthorne effect (Landsberger, 1958) is that in which individuals modify their behaviour based upon the readily apparent fact that they are being observed in a given situation. The presence of a visible observer or monitoring device causes people to behave other than they usually would, thereby decreasing the accuracy of any recorded research data. Likewise, a system which requires that participants wear or interact with unwieldy equipment is less easy to use.

Therefore, the ideal system for monitoring human activity is one which is cheap, unobtrusive, and sufficiently accurate. Device-free passive localisation (DfPL) is one class of such human activity monitoring systems. Passive localization differs from active localisation insofar as that in order to perform active localisation, participants or users must carry some form of monitoring device upon their person. With reference to the previous examples, an automatic door is a passive sensor, whereas a locator bracelet detector is an active sensor. Cost for such devices may be nominal, but any approach that requires active participation will be intrusive to some extent. DfPL avoids these concerns by functioning without any such requirement – it is “device free” in that no equipment must be carried by participants. In DfPL, the sensing component takes advantage of the absorption and scattering effects of human bodies on RF radiation, particularly around the range of 2.4 GHz, which is common to all Wi-Fi standards, and which also is strongly absorbed by water, of which human bodies are largely composed. Environmental blockage, such as furniture and human bodies in the vicinity of the receiver or transmitter cause deep fades that might limit the range and the performance of short-range wireless systems (Deak et al., 2013).

Although this is an issue for maintenance of a connection which becomes obstructed by a human body, it does mean that a wireless link between a device and an AP has some degree of ability to sense changes in the level of obstruction between the two. Previous studies have shown that the signal level exceeds the average level when the person is close to the link but not blocking it (Kara and Bertoni, 2006; Deak et al., 2014), which means that as a person enters, occludes, and leaves the main body of a signal path, their passage creates a series of perturbations in the Received Signal Strength Indication (RSSI) recorded on receipt of a data packet that can be used to infer motion through that path.

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