dc.creator | Imamoglu, Nevrez | es |
dc.creator | Dorronzoro Zubiete, Enrique | es |
dc.creator | Wei, Zhixuan | es |
dc.creator | Shi, Huangjun | es |
dc.creator | Sekine, Masashi | es |
dc.creator | González, José | es |
dc.creator | Gu, Dongyun | es |
dc.creator | Chen, Weidong | es |
dc.creator | Yu, Wenwei | es |
dc.date.accessioned | 2017-02-14T09:10:29Z | |
dc.date.available | 2017-02-14T09:10:29Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Imamoglu, N., Dorronzoro Zubiete, E., Wei, Z., Shi, H., Sekine, M., González, J.,...,Yu, W. (2014). Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking. Scientific World Journal, 2014 (Article ID 280207) | |
dc.identifier.issn | 2356-6140 | es |
dc.identifier.uri | http://hdl.handle.net/11441/54043 | |
dc.description.abstract | Our research is focused on the development of an at-home health care biomonitoringmobile robot for the people in demand. Main
task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning
in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the
robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color
tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination
values and tracking distance intervals.Then, regarding subject safety and continuous robot based subject tracking, various control
parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for
different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by
making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was
tested on different walking patterns with different subjects, and the results showed high recognition accuracy. | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Hindawi Publishing Corporation | es |
dc.relation.ispartof | Scientific World Journal, 2014 (Article ID 280207) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Tecnología Electrónica | es |
dc.relation.publisherversion | https://www.hindawi.com/journals/tswj/2014/280207/ | es |
dc.identifier.doi | 10.1155/2014/280207 | es |
dc.contributor.group | Universidad de Sevilla. TIC022: Tecnologías para la asistencia, la investigación y la salud | es |
idus.format.extent | 22 | es |
dc.journaltitle | The Scientific World Journal | es |
dc.publication.volumen | 2014 | es |
dc.publication.issue | Article ID 280207 | es |