Context transitions: user identification and comparison of mobile device motion data
Lovett, T. and O'Neill, E., 2011. Context transitions: user identification and comparison of mobile device motion data. In: Activity Context Representation: Techniques and Languages - Papers from the 2011 AAAI Workshop, Technical Report. Vol. WS-11-04. El Segundo, CA.: AI Access Foundation, pp. 42-47. (AAAI Workshop - Technical Report)
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In this paper, we study a time-critical facet of context-awareness: context transitions, which we model as changes in specific context types over time, e.g., activity or location. We present results from a user-centred field study involving participant interviews and motion data capture from two mobile device sensors: the accelerometer and magnetic field sensor. The results show how the participants subjectively interpret their daily context transitions with variable granularity, and a comparison of these context transitions with mobile device motion data shows how the motion data poorly reflect the identified transitions. The results imply that care should be taken when representing and modelling users' subjective interpretations of context, as well as the objective nature of context sensors. Furthermore, processing and usability trade-offs should be made if real-time on-device transition detection is to be implemented.
|Item Type||Book Sections|
|Creators||Lovett, T.and O'Neill, E.|
|Departments||Faculty of Science > Computer Science|
|Additional Information||2011 AAAI Workshop, 7-8 August 2011, San Francisco, CA.|
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