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Open AccessArticle

Inferring Human Activity in Mobile Devices by Computing Multiple Contexts

1
Conrad Blucher Institute for Surveying & Science, Texas A&M University Corpus Christi, Corpus Christi, TX 78412-5868, USA
2
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
3
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
Sensors 2015, 15(9), 21219-21238; https://doi.org/10.3390/s150921219
Received: 8 July 2015 / Revised: 31 July 2015 / Accepted: 25 August 2015 / Published: 28 August 2015
(This article belongs to the Section Remote Sensors)
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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MDPI and ACS Style

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. https://doi.org/10.3390/s150921219

AMA Style

Chen R, Chu T, Liu K, Liu J, Chen Y. Inferring Human Activity in Mobile Devices by Computing Multiple Contexts. Sensors. 2015; 15(9):21219-21238. https://doi.org/10.3390/s150921219

Chicago/Turabian Style

Chen, Ruizhi; Chu, Tianxing; Liu, Keqiang; Liu, Jingbin; Chen, Yuwei. 2015. "Inferring Human Activity in Mobile Devices by Computing Multiple Contexts" Sensors 15, no. 9: 21219-21238. https://doi.org/10.3390/s150921219

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