A survey on ontologies for human behavior recognition-Reference-Cited by-同舟云学术

A survey on ontologies for human behavior recognition

Published:2014-04 Issue:4 Volume:46 Page:1-33
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Rodríguez Natalia Díaz¹,Cuéllar M. P.²,Lilius Johan¹,Calvo-Flores Miguel Delgado²

Affiliation:

1. Turku Centre for Computer Science (TUCS), Department of Information Technologies, Åbo Akademi University, Turku, Finland

2. Department of Computer Science and Artificial Intelligence, University of Granada, Spain

Abstract

Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques. We focus on context ontologies whose ultimate goal is the tracking of human behavior. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modeling daily human behaviors, are identified as future challenges.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/2523819

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