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Sensors 2013, 13(2), 1402-1424; doi:10.3390/s130201402
Article

Human Behavior Cognition Using Smartphone Sensors

1,* , 1
, 1,2
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 and 3
1 Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland 2 Conrad Blucher Institute for Surveying & Science, Texas A&M University Corpus Christi, Corpus Christi, TX 78412, USA 3 Psychology of Evolving Media and Technology Research Group, Institute of Behavioural Sciences, University of Helsinki, 00014 Helsinki, Finland
* Author to whom correspondence should be addressed.
Received: 28 November 2012 / Revised: 26 December 2012 / Accepted: 15 January 2013 / Published: 24 January 2013
(This article belongs to the Section Physical Sensors)
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Abstract

This research focuses on sensing context, modeling human behavior and developing a new architecture for a cognitive phone platform. We combine the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior. Contexts in this research are abstracted as a Context Pyramid which includes six levels: Raw Sensor Data, Physical Parameter, Features/Patterns, Simple Contextual Descriptors, Activity-Level Descriptors, and Rich Context. To achieve implementation of the Context Pyramid on a cognitive phone, three key technologies are utilized: ubiquitous positioning, motion recognition, and human behavior modeling. Preliminary tests indicate that we have successfully achieved the Activity-Level Descriptors level with our LoMoCo (Location-Motion-Context) model. Location accuracy of the proposed solution is up to 1.9 meters in corridor environments and 3.5 meters in open spaces. Test results also indicate that the motion states are recognized with an accuracy rate up to 92.9% using a Least Square-Support Vector Machine (LS-SVM) classifier.
Keywords: sensing; location; motion recognition; LS-SVM; cognitive phone; human behavior modeling sensing; location; motion recognition; LS-SVM; cognitive phone; human behavior modeling
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.

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MDPI and ACS Style

Pei, L.; Guinness, R.; Chen, R.; Liu, J.; Kuusniemi, H.; Chen, Y.; Chen, L.; Kaistinen, J. Human Behavior Cognition Using Smartphone Sensors. Sensors 2013, 13, 1402-1424.

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