Reciprocal estimation of pedestrian location and motion state toward a smartphone geo-context computing solution
| dc.contributor.author | Liu, Jingbin | |
| dc.contributor.author | Zhu, Lingli | |
| dc.contributor.author | Wang, Yunsheng | |
| dc.contributor.author | Liang, Xinlian | |
| dc.contributor.author | Hyyppä, Juha | |
| dc.contributor.author | Chu, Tianxing | |
| dc.contributor.author | Liu, Keqiang | |
| dc.contributor.author | Chen, Ruizhi | |
| dc.date.accessioned | 2021-10-28T19:16:43Z | |
| dc.date.available | 2021-10-28T19:16:43Z | |
| dc.date.issued | 2015-06-15 | |
| dc.description.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. | en_US |
| dc.description.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. | |
| dc.identifier.citation | Liu, J., Zhu, L., Wang, Y., Liang, X., Hyyppä, J., Chu, T., Liu, K. and Chen, R., 2015. Reciprocal estimation of pedestrian location and motion state toward a smartphone geo-context computing solution. Micromachines, 6(6), pp.699-717. | en_US |
| dc.identifier.citation | Liu, J., Zhu, L., Wang, Y., Liang, X., Hyyppä, J., Chu, T., Liu, K. and Chen, R., 2015. Reciprocal estimation of pedestrian location and motion state toward a smartphone geo-context computing solution. Micromachines, 6(6), pp.699-717. | |
| dc.identifier.doi | https://doi.org/10.3390/mi6060699 | |
| dc.identifier.uri | https://hdl.handle.net/1969.6/89921 | |
| dc.language.iso | en_US | en_US |
| dc.language.iso | en_US | |
| dc.publisher | MDPI | en_US |
| dc.publisher | MDPI | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | geo-context sensing | en_US |
| dc.subject | geospatial | en_US |
| dc.subject | geospatial computing | en_US |
| dc.subject | pedestrian navigation | en_US |
| dc.subject | indoor positioning | en_US |
| dc.subject | activity recognition | en_US |
| dc.subject | mobile computing | en_US |
| dc.subject | smartphone navigation | en_US |
| dc.subject | geo-context sensing | |
| dc.subject | geospatial | |
| dc.subject | geospatial computing | |
| dc.subject | pedestrian navigation | |
| dc.subject | indoor positioning | |
| dc.subject | activity recognition | |
| dc.subject | mobile computing | |
| dc.subject | smartphone navigation | |
| dc.title | Reciprocal estimation of pedestrian location and motion state toward a smartphone geo-context computing solution | en_US |
| dc.title | Reciprocal estimation of pedestrian location and motion state toward a smartphone geo-context computing solution | |
| dc.type | Article | en_US |
| dc.type | Article |