An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities
Activities 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, w...
| Главные авторы: | , |
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| Формат: | Conference Paper |
| Язык: | English |
| Опубликовано: |
2016
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| Предметы: | |
| Online-ссылка: | https://hdl.handle.net/10356/81332 http://hdl.handle.net/10220/40734 http://www.ifaamas.org/proceedings.html |
| _version_ | 1826110059035230208 |
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| author | Gao, Shan Tan, Ah-Hwee |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Gao, Shan Tan, Ah-Hwee |
| author_sort | Gao, Shan |
| collection | NTU |
| description | Activities 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. |
| first_indexed | 2024-10-01T02:28:15Z |
| format | Conference Paper |
| id | ntu-10356/81332 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:28:15Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/813322020-11-01T04:43:00Z An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities Gao, Shan Tan, Ah-Hwee School of Computer Science and Engineering Interdisciplinary Graduate School (IGS) International Conference on Autonomous Agents and Multiagent Systems 2016 Activity pattern spatiotemporal features Fusion ART Activities 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. NRF (Natl Research Foundation, S’pore) Published version 2016-06-22T03:43:31Z 2019-12-06T14:28:38Z 2016-06-22T03:43:31Z 2019-12-06T14:28:38Z 2016 Conference Paper Gao, 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. https://hdl.handle.net/10356/81332 http://hdl.handle.net/10220/40734 http://www.ifaamas.org/proceedings.html en © 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. 2 p. application/pdf |
| spellingShingle | Activity pattern spatiotemporal features Fusion ART Gao, Shan Tan, Ah-Hwee An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities |
| title | An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities |
| title_full | An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities |
| title_fullStr | An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities |
| title_full_unstemmed | An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities |
| title_short | An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities |
| title_sort | autonomous agent for learning spatiotemporal models of human daily activities |
| topic | Activity pattern spatiotemporal features Fusion ART |
| url | https://hdl.handle.net/10356/81332 http://hdl.handle.net/10220/40734 http://www.ifaamas.org/proceedings.html |
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