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Micromachines 2015, 6(6), 699-717; doi:10.3390/mi6060699

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

1
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Masala 02431, Finland
2
Centre of Excellence in Laser Scanning Research, Academy of Finland, Finland
3
Conrad Blucher Institute of Surveying & Science, Texas A & M University Corpus Christi, Corpus Christi, TX 78412-5868, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Naser El-Sheimy and Aboelmagd Noureldin
Received: 26 February 2015 / Accepted: 10 June 2015 / Published: 15 June 2015
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
View Full-Text   |   Download PDF [4745 KB, uploaded 15 June 2015]   |  

Abstract

The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed. View Full-Text
Keywords: geo-context sensing; geospatial computing; pedestrian navigation; indoor positioning; activity recognition; mobile computing; smartphone navigation geo-context sensing; geospatial computing; pedestrian navigation; indoor positioning; activity recognition; mobile computing; smartphone navigation
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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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MDPI and ACS Style

Liu, J.; Zhu, L.; Wang, Y.; Liang, X.; Hyyppä, J.; Chu, T.; Liu, K.; Chen, R. Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution. Micromachines 2015, 6, 699-717.

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