Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings

Research Article

Smartphone-Based Detection of Location Changes Using WiFi Data

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  • @INPROCEEDINGS{10.1007/978-3-319-58877-3_22,
        author={Anja Exler and Matthias Urschel and Andrea Schankin and Michael Beigl},
        title={Smartphone-Based Detection of Location Changes Using WiFi Data},
        proceedings={Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2017},
        month={6},
        keywords={Mobile sensing WiFi Location changes},
        doi={10.1007/978-3-319-58877-3_22}
    }
    
  • Anja Exler
    Matthias Urschel
    Andrea Schankin
    Michael Beigl
    Year: 2017
    Smartphone-Based Detection of Location Changes Using WiFi Data
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-319-58877-3_22
Anja Exler1,*, Matthias Urschel1,*, Andrea Schankin1,*, Michael Beigl1,*
  • 1: TECO
*Contact email: exler@teco.edu, urschel@teco.edu, schankin@teco.edu, michael@teco.edu

Abstract

Context information, in particular location changes as indicator for motoric activity, are indicators for state changes of patients suffering from affective disorders. Traditionally, such information is assessed via self-report questionnaires. However, this approach is obtrusive and requires direct involvement of the patient. Related work already started to rely on unobtrusively gathered smartphone data. Despite its ubiquitousness, WiFi data was barely considered yet. Due to the increasing availability of public hot spots we want to focus on this data source. We investigate the usefulness of WiFi data in two use cases: detect location changes and estimate the number of nearby persons. In a two-week study we captured MAC addresses, WiFi SSIDs and timestamps to identify current location and location changes of ten subjects in a five minute interval. We achieved a recall of 98% for location changes which proves the usability of WiFi data for this purpose. We confirm a basic feasibility of using WiFi data for unobtrusive, opportune and energy-efficient detection of location changes.