Human activity recognition based on surrounding things

Naoharu Yamada, Kenji Sakamoto, Goro Kunito, Kenichi Yamazaki, Satoshi Tanaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper proposes human activity recognition based on the actual semantics of the human's current location. Since predefining the semantics of location is inadequate to identify human activities, we process information about things to automatically identify the semantics based on the concept of affordance. Ontology is used to deal with the various possible representations of things detected by RFIDs, and a multi-class Naïve Bayesian approach is used to detect multiple actual semantics from the terms representing things. Our approach is suitable for automatically detecting possible activities under a variety of characteristics of things including polysemy and variability. Preliminary experiments on manually collected datasets of things demonstrated its noise tolerance and ability to rapidly detect multiple actual semantics from existing things.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages1-10
Number of pages10
Volume3823 LNCS
Publication statusPublished - 2005
Externally publishedYes
EventEUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES - Nagasaki
Duration: 2005 Dec 62005 Dec 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3823 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherEUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES
CityNagasaki
Period05/12/605/12/9

Fingerprint

Semantics
Radio frequency identification (RFID)
Ontology
Experiments

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Yamada, N., Sakamoto, K., Kunito, G., Yamazaki, K., & Tanaka, S. (2005). Human activity recognition based on surrounding things. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3823 LNCS, pp. 1-10). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3823 LNCS).

Human activity recognition based on surrounding things. / Yamada, Naoharu; Sakamoto, Kenji; Kunito, Goro; Yamazaki, Kenichi; Tanaka, Satoshi.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3823 LNCS 2005. p. 1-10 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3823 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yamada, N, Sakamoto, K, Kunito, G, Yamazaki, K & Tanaka, S 2005, Human activity recognition based on surrounding things. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3823 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3823 LNCS, pp. 1-10, EUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES, Nagasaki, 05/12/6.
Yamada N, Sakamoto K, Kunito G, Yamazaki K, Tanaka S. Human activity recognition based on surrounding things. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3823 LNCS. 2005. p. 1-10. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Yamada, Naoharu ; Sakamoto, Kenji ; Kunito, Goro ; Yamazaki, Kenichi ; Tanaka, Satoshi. / Human activity recognition based on surrounding things. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3823 LNCS 2005. pp. 1-10 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{b063388158ae4720992a08f3b00b50f0,
title = "Human activity recognition based on surrounding things",
abstract = "This paper proposes human activity recognition based on the actual semantics of the human's current location. Since predefining the semantics of location is inadequate to identify human activities, we process information about things to automatically identify the semantics based on the concept of affordance. Ontology is used to deal with the various possible representations of things detected by RFIDs, and a multi-class Na{\"i}ve Bayesian approach is used to detect multiple actual semantics from the terms representing things. Our approach is suitable for automatically detecting possible activities under a variety of characteristics of things including polysemy and variability. Preliminary experiments on manually collected datasets of things demonstrated its noise tolerance and ability to rapidly detect multiple actual semantics from existing things.",
author = "Naoharu Yamada and Kenji Sakamoto and Goro Kunito and Kenichi Yamazaki and Satoshi Tanaka",
year = "2005",
language = "English",
isbn = "3540308032",
volume = "3823 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "1--10",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Human activity recognition based on surrounding things

AU - Yamada, Naoharu

AU - Sakamoto, Kenji

AU - Kunito, Goro

AU - Yamazaki, Kenichi

AU - Tanaka, Satoshi

PY - 2005

Y1 - 2005

N2 - This paper proposes human activity recognition based on the actual semantics of the human's current location. Since predefining the semantics of location is inadequate to identify human activities, we process information about things to automatically identify the semantics based on the concept of affordance. Ontology is used to deal with the various possible representations of things detected by RFIDs, and a multi-class Naïve Bayesian approach is used to detect multiple actual semantics from the terms representing things. Our approach is suitable for automatically detecting possible activities under a variety of characteristics of things including polysemy and variability. Preliminary experiments on manually collected datasets of things demonstrated its noise tolerance and ability to rapidly detect multiple actual semantics from existing things.

AB - This paper proposes human activity recognition based on the actual semantics of the human's current location. Since predefining the semantics of location is inadequate to identify human activities, we process information about things to automatically identify the semantics based on the concept of affordance. Ontology is used to deal with the various possible representations of things detected by RFIDs, and a multi-class Naïve Bayesian approach is used to detect multiple actual semantics from the terms representing things. Our approach is suitable for automatically detecting possible activities under a variety of characteristics of things including polysemy and variability. Preliminary experiments on manually collected datasets of things demonstrated its noise tolerance and ability to rapidly detect multiple actual semantics from existing things.

UR - http://www.scopus.com/inward/record.url?scp=33744926185&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33744926185&partnerID=8YFLogxK

M3 - Conference contribution

SN - 3540308032

SN - 9783540308034

VL - 3823 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

EP - 10

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -