Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81332
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGao, Shanen
dc.contributor.authorTan, Ah-Hweeen
dc.date.accessioned2016-06-22T03:43:31Zen
dc.date.accessioned2019-12-06T14:28:38Z-
dc.date.available2016-06-22T03:43:31Zen
dc.date.available2019-12-06T14:28:38Z-
dc.date.issued2016en
dc.identifier.citationGao, S. & Tan, A.-H. (2016). An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities. 2016 International Conference on Autonomous Agents and Multiagent Systems, in press.en
dc.identifier.urihttps://hdl.handle.net/10356/81332-
dc.description.abstractActivities of Daily Living (ADLs) refer to activities performed by individuals on a daily basis. As ADLs are indicatives of a person's habits, lifestyle, and well being, learning the knowledge of people's ADL routine has great values in the healthcare and consumer domains. In this paper, we propose an autonomous agent, named Agent for Spatia-Temporal Activity Pattern Modeling (ASTAPM), being able to learn spatial and temporal patterns of human ADLs. ASTAPM utilises a self-organizing neural network model named Spatiotemporal - Adaptive Resonance Theory (ST-ART). ST-ART is capable of integrating multimodal contextual information, involving the time and space, wherein the ADL are performed. Empirical experiments have been conducted to assess the performance of ASTAPM in terms of accuracy and generalization.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.format.extent2 p.en
dc.language.isoenen
dc.rights© 2016 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). This paper was published in Autonomous Agents and Multiagent Systems Conference Proceedings 2016 and is made available as an electronic reprint (preprint) with permission of International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). The published version is available at: [http://www.ifaamas.org/proceedings.html]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en
dc.subjectActivity patternen
dc.subjectspatiotemporal featuresen
dc.subjectFusion ARTen
dc.titleAn Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activitiesen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en
dc.contributor.conferenceInternational Conference on Autonomous Agents and Multiagent Systems 2016en
dc.description.versionPublished versionen
dc.identifier.urlhttp://www.ifaamas.org/proceedings.htmlen
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:IGS Conference Papers
SCSE Conference Papers
Files in This Item:
File Description SizeFormat 
Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities.pdf104.07 kBAdobe PDFThumbnail
View/Open

Page view(s)

270
checked on Oct 26, 2020

Download(s)

105
checked on Oct 26, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.