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An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities


Authors: S. Gao and A.-H. Tan
Title: An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities
Abstract: 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.
Keywords: Fusion ART; Activity pattern; Spatiotemporal features
Conference Name: 15th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS'16)
Location: Singapore, Singapore
Publisher: IFAAMAS
Year: 2016
Accepted PDF File: An_Autonomous_Agent_for_Learning_Spatiotemporal_Models_of_Human_Daily_Activities_accepted.pdf
Permanent Link: http://www.ifaamas.org/Proceedings/aamas2016/pdfs/p1453.pdf
Reference: S. Gao and A.-H. Tan, “An autonomous agent for learning spatiotemporal models of human daily activities,” in Proceedings of the 15th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS’16). IFAAMAS, May 2016, pp. 1453–1454.
bibtex: 
@inproceedings{LILY-c81, 
    author	= {Gao, Shan and Tan, Ah-Hwee},
    title	= {An Autonomous Agent for Learning Spatiotemporal Models of Human Daily Activities},  
    booktitle	= {Proceedings of the 15th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS'16)}, 
    year		= {2016}, 
    month	= {May}, 
    pages	= {1453-1454}, 
    location	= {Singapore, Singapore},
    publisher	= {IFAAMAS},
 }