An autonomous agent for learning spatiotemporal models of human daily activities

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, we propose...

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Bibliographic Details
Main Authors: GAO, Shan, TAN, Ah-Hwee
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
Fusion ART
Activity pattern
spatiotemporal features
Artificial Intelligence and Robotics
Databases and Information Systems
Graphics and Human Computer Interfaces
Online Access:https://ink.library.smu.edu.sg/sis_research/5611
https://ink.library.smu.edu.sg/context/sis_research/article/6614/viewcontent/Autonomous_Agent_for_Learning_Spatiotemporal_Models_of_Human_Daily_Activities.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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