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Sensors 2015, 15(9), 21219-21238;

Inferring Human Activity in Mobile Devices by Computing Multiple Contexts

Conrad Blucher Institute for Surveying & Science, Texas A&M University Corpus Christi, Corpus Christi, TX 78412-5868, USA
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Masala 02431, Finland
Author to whom correspondence should be addressed.
Academic Editor: Andrea Sanna
Received: 8 July 2015 / Revised: 31 July 2015 / Accepted: 25 August 2015 / Published: 28 August 2015
(This article belongs to the Section Remote Sensors)
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This paper introduces a framework for inferring human activities in mobile devices by computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A spatial context is a significant location that is defined as a geofence, which can be a node associated with a circle, or a polygon; a temporal context contains time-related information that can be e.g., a local time tag, a time difference between geographical locations, or a timespan; a spatiotemporal context is defined as a dwelling length at a particular spatial context; and a user context includes user-related information that can be the user’s mobility contexts, environmental contexts, psychological contexts or social contexts. Using the measurements of the built-in sensors and radio signals in mobile devices, we can snapshot a contextual tuple for every second including aforementioned contexts. Giving a contextual tuple, the framework evaluates the posteriori probability of each candidate activity in real-time using a Naïve Bayes classifier. A large dataset containing 710,436 contextual tuples has been recorded for one week from an experiment carried out at Texas A&M University Corpus Christi with three participants. The test results demonstrate that the multi-context solution significantly outperforms the spatial-context-only solution. A classification accuracy of 61.7% is achieved for the spatial-context-only solution, while 88.8% is achieved for the multi-context solution. View Full-Text
Keywords: human activity recognition; mobile context computation; location awareness; smartphone positioning human activity recognition; mobile context computation; location awareness; smartphone positioning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Chen, R.; Chu, T.; Liu, K.; Liu, J.; Chen, Y. Inferring Human Activity in Mobile Devices by Computing Multiple Contexts. Sensors 2015, 15, 21219-21238.

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