Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2016

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

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016, Siingapore, May 9-13

First Page

1453

Last Page

1454

ISBN

9781450342391

Publisher

IFAAMAS

City or Country

Singapore

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