A rule-based approach to activity recognition

Pitchakan Theekakul; Surapa Thiemjarus; Ekawit Nantajeewarawat; Thepchai Supnithi; Kaoru Hirota

doi:10.1007/978-3-642-24788-0_19

A rule-based approach to activity recognition

Pitchakan Theekakul^*
, Surapa Thiemjarus
, Ekawit Nantajeewarawat
, Thepchai Supnithi
, Kaoru Hirota

^*Corresponding author for this work

Thammasat University
National Science and Technology Development Agency Thailand
Tokyo Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

17 Citations (Scopus)

Abstract

This paper presents a rule-based framework for activity classification and illustrates how domain-specific expert knowledge and observation of data in its feature space can be used for rule construction. To demonstrate its practical value, the framework is applied on datasets collected during an orientation-independent activity recognition experiment. Through an implementation based on the Java Expert System Shell (JESS), two types of rules are compared: rules that are specifically constructed for each individual device orientation and those constructed without assuming any prior knowledge on device orientations. Overall accuracy improvements of 7.97% and 9.25% are observed on training and test datasets when orientation-specific rules are used.

Original language	English
Title of host publication	Knowledge, Information, and Creativity Support Systems - 5th International Conference, KICSS 2010, Revised Selected Papers
Pages	204-215
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-24788-0_19
Publication status	Published - 2011
Externally published	Yes
Event	5th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2010 - Chiang Mai, Thailand Duration: 25 Nov 2010 → 27 Nov 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6746 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2010
Country/Territory	Thailand
City	Chiang Mai
Period	25/11/10 → 27/11/10

Keywords

Rule-based reasoning
activity recognition
body sensor network
device-orientation detection
rule learning

Access to Document

10.1007/978-3-642-24788-0_19

Cite this

Theekakul, P., Thiemjarus, S., Nantajeewarawat, E., Supnithi, T., & Hirota, K. (2011). A rule-based approach to activity recognition. In Knowledge, Information, and Creativity Support Systems - 5th International Conference, KICSS 2010, Revised Selected Papers (pp. 204-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6746 LNAI). https://doi.org/10.1007/978-3-642-24788-0_19

Theekakul, Pitchakan ; Thiemjarus, Surapa ; Nantajeewarawat, Ekawit et al. / A rule-based approach to activity recognition. Knowledge, Information, and Creativity Support Systems - 5th International Conference, KICSS 2010, Revised Selected Papers. 2011. pp. 204-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper presents a rule-based framework for activity classification and illustrates how domain-specific expert knowledge and observation of data in its feature space can be used for rule construction. To demonstrate its practical value, the framework is applied on datasets collected during an orientation-independent activity recognition experiment. Through an implementation based on the Java Expert System Shell (JESS), two types of rules are compared: rules that are specifically constructed for each individual device orientation and those constructed without assuming any prior knowledge on device orientations. Overall accuracy improvements of 7.97\% and 9.25\% are observed on training and test datasets when orientation-specific rules are used.",

keywords = "Rule-based reasoning, activity recognition, body sensor network, device-orientation detection, rule learning",

author = "Pitchakan Theekakul and Surapa Thiemjarus and Ekawit Nantajeewarawat and Thepchai Supnithi and Kaoru Hirota",

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booktitle = "Knowledge, Information, and Creativity Support Systems - 5th International Conference, KICSS 2010, Revised Selected Papers",

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Theekakul, P, Thiemjarus, S, Nantajeewarawat, E, Supnithi, T & Hirota, K 2011, A rule-based approach to activity recognition. in Knowledge, Information, and Creativity Support Systems - 5th International Conference, KICSS 2010, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6746 LNAI, pp. 204-215, 5th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2010, Chiang Mai, Thailand, 25/11/10. https://doi.org/10.1007/978-3-642-24788-0_19

A rule-based approach to activity recognition. / Theekakul, Pitchakan; Thiemjarus, Surapa; Nantajeewarawat, Ekawit et al.
Knowledge, Information, and Creativity Support Systems - 5th International Conference, KICSS 2010, Revised Selected Papers. 2011. p. 204-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6746 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A rule-based approach to activity recognition

AU - Theekakul, Pitchakan

AU - Thiemjarus, Surapa

AU - Nantajeewarawat, Ekawit

AU - Supnithi, Thepchai

AU - Hirota, Kaoru

PY - 2011

Y1 - 2011

N2 - This paper presents a rule-based framework for activity classification and illustrates how domain-specific expert knowledge and observation of data in its feature space can be used for rule construction. To demonstrate its practical value, the framework is applied on datasets collected during an orientation-independent activity recognition experiment. Through an implementation based on the Java Expert System Shell (JESS), two types of rules are compared: rules that are specifically constructed for each individual device orientation and those constructed without assuming any prior knowledge on device orientations. Overall accuracy improvements of 7.97% and 9.25% are observed on training and test datasets when orientation-specific rules are used.

AB - This paper presents a rule-based framework for activity classification and illustrates how domain-specific expert knowledge and observation of data in its feature space can be used for rule construction. To demonstrate its practical value, the framework is applied on datasets collected during an orientation-independent activity recognition experiment. Through an implementation based on the Java Expert System Shell (JESS), two types of rules are compared: rules that are specifically constructed for each individual device orientation and those constructed without assuming any prior knowledge on device orientations. Overall accuracy improvements of 7.97% and 9.25% are observed on training and test datasets when orientation-specific rules are used.

KW - Rule-based reasoning

KW - activity recognition

KW - body sensor network

KW - device-orientation detection

KW - rule learning

UR - https://www.scopus.com/pages/publications/81755166831

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DO - 10.1007/978-3-642-24788-0_19

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AN - SCOPUS:81755166831

SN - 9783642247873

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

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BT - Knowledge, Information, and Creativity Support Systems - 5th International Conference, KICSS 2010, Revised Selected Papers

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ER -

Theekakul P, Thiemjarus S, Nantajeewarawat E, Supnithi T, Hirota K. A rule-based approach to activity recognition. In Knowledge, Information, and Creativity Support Systems - 5th International Conference, KICSS 2010, Revised Selected Papers. 2011. p. 204-215. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-24788-0_19

A rule-based approach to activity recognition

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