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dc.contributor.authorHuang, Sz-Pinen_US
dc.contributor.authorQiu, Jun-Weien_US
dc.contributor.authorLo, Chj-Chungen_US
dc.contributor.authorTseng, Yu-Cheeen_US
dc.date.accessioned2014-12-08T15:36:53Z-
dc.date.available2014-12-08T15:36:53Z-
dc.date.issued2014-01-01en_US
dc.identifier.isbn978-1-4799-4959-5en_US
dc.identifier.issnen_US
dc.identifier.urihttp://dx.doi.org/10.1109/BSN.2014.15en_US
dc.identifier.urihttp://hdl.handle.net/11536/25279-
dc.description.abstractIndoor positioning has been intensively studied recently due to the exploding demands of indoor mobile applications. While numerous works have employed wireless signals or dead-reckoning techniques, wearable computing poses new opportunities as well as challenges to the localization problem. This research studies the wearable localization problem by proposing a particle filter-based scheme to fuse the inputs from wearable inertial and visual sensors on human body. Specifically, the filter takes inertial measurements, wireless signals, visual landmarks, and indoor floor plans as inputs for location tracking. The inertial signals imply human body movements, the wireless signals indicate a rough absolute region inside a building, while the visual landmarks provide relative angles viewed from particular positions to these markers. Furthermore, a head-mounted display provides intuitive and friendly interfaces to users. The proposed system has also been prototyped and tested in our campus, and the experiments demonstrate an average localization error of about one meter.en_US
dc.language.isoen_USen_US
dc.titleWearable Localization by Particle Filter with the Assistance of Inertial and Visual Sensorsen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/BSN.2014.15en_US
dc.identifier.journal2014 11TH INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN)en_US
dc.citation.volumeen_US
dc.citation.issueen_US
dc.citation.spage52en_US
dc.citation.epage57en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000343599400010-
Appears in Collections:Conferences Paper