Searching for temporal patterns in AmI sensor data

Romain Tavenard; Albert A. Salah; Eric J. Pauwels

doi:10.1007/978-3-540-85379-4_7

Searching for temporal patterns in AmI sensor data

Romain Tavenard, Albert A. Salah, Eric J. Pauwels

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

Abstract

Anticipation is a key property of human-human communication, and it is highly desirable for ambient environments to have the means of anticipating events to create a feeling of responsiveness and intelligence in the user. In a home or work environment, a great number of low-cost sensors can be deployed to detect simple events: the passing of a person, the usage of an object, the opening of a door. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. Using a testbed that we have developed for this purpose, we first contrast current approaches to the problem. We then extend the best of these approaches, the T-Pattern algorithm, with Gaussian Mixture Models, to obtain a fast and robust algorithm to find patterns in temporal data. Our algorithm can be used to anticipate future events, as well as to detect unexpected events as they occur.

Original language	English
Title of host publication	Constructing Ambient Intelligence - AmI 2007 Workshops, Revised Papers
Editors	Boris Ruyter, Emile Aarts, Manfred Tscheligi, Anind Dey, Hans Gellersen, Bernt Schiele, Alejandro Buchmann, Max Muhlhauser, Erwin Aitenbichler, Reiner Wichert, Alois Ferscha
Publisher	Springer
Pages	53-62
Number of pages	10
ISBN (Print)	9783540853787
DOIs	https://doi.org/10.1007/978-3-540-85379-4_7
Publication status	Published - 2008
Event	European Conference on Ambient Intelligence, AmI 2007 - Darmstadt, Germany Duration: 7 Nov 2007 → 10 Nov 2007

Publication series

Name	Communications in Computer and Information Science
Volume	11
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	European Conference on Ambient Intelligence, AmI 2007
Country/Territory	Germany
City	Darmstadt
Period	7/11/07 → 10/11/07

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2008.

Access to Document

10.1007/978-3-540-85379-4_7

Cite this

Tavenard, R., Salah, A. A., & Pauwels, E. J. (2008). Searching for temporal patterns in AmI sensor data. In B. Ruyter, E. Aarts, M. Tscheligi, A. Dey, H. Gellersen, B. Schiele, A. Buchmann, M. Muhlhauser, E. Aitenbichler, R. Wichert, & A. Ferscha (Eds.), Constructing Ambient Intelligence - AmI 2007 Workshops, Revised Papers (pp. 53-62). (Communications in Computer and Information Science; Vol. 11). Springer. https://doi.org/10.1007/978-3-540-85379-4_7

Tavenard, Romain ; Salah, Albert A. ; Pauwels, Eric J. / Searching for temporal patterns in AmI sensor data. Constructing Ambient Intelligence - AmI 2007 Workshops, Revised Papers. editor / Boris Ruyter ; Emile Aarts ; Manfred Tscheligi ; Anind Dey ; Hans Gellersen ; Bernt Schiele ; Alejandro Buchmann ; Max Muhlhauser ; Erwin Aitenbichler ; Reiner Wichert ; Alois Ferscha. Springer, 2008. pp. 53-62 (Communications in Computer and Information Science).

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title = "Searching for temporal patterns in AmI sensor data",

abstract = "Anticipation is a key property of human-human communication, and it is highly desirable for ambient environments to have the means of anticipating events to create a feeling of responsiveness and intelligence in the user. In a home or work environment, a great number of low-cost sensors can be deployed to detect simple events: the passing of a person, the usage of an object, the opening of a door. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. Using a testbed that we have developed for this purpose, we first contrast current approaches to the problem. We then extend the best of these approaches, the T-Pattern algorithm, with Gaussian Mixture Models, to obtain a fast and robust algorithm to find patterns in temporal data. Our algorithm can be used to anticipate future events, as well as to detect unexpected events as they occur.",

author = "Romain Tavenard and Salah, {Albert A.} and Pauwels, {Eric J.}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2008.; European Conference on Ambient Intelligence, AmI 2007 ; Conference date: 07-11-2007 Through 10-11-2007",

year = "2008",

doi = "10.1007/978-3-540-85379-4_7",

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isbn = "9783540853787",

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editor = "Boris Ruyter and Emile Aarts and Manfred Tscheligi and Anind Dey and Hans Gellersen and Bernt Schiele and Alejandro Buchmann and Max Muhlhauser and Erwin Aitenbichler and Reiner Wichert and Alois Ferscha",

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Tavenard, R, Salah, AA & Pauwels, EJ 2008, Searching for temporal patterns in AmI sensor data. in B Ruyter, E Aarts, M Tscheligi, A Dey, H Gellersen, B Schiele, A Buchmann, M Muhlhauser, E Aitenbichler, R Wichert & A Ferscha (eds), Constructing Ambient Intelligence - AmI 2007 Workshops, Revised Papers. Communications in Computer and Information Science, vol. 11, Springer, pp. 53-62, European Conference on Ambient Intelligence, AmI 2007, Darmstadt, Germany, 7/11/07. https://doi.org/10.1007/978-3-540-85379-4_7

Searching for temporal patterns in AmI sensor data. / Tavenard, Romain; Salah, Albert A.; Pauwels, Eric J.
Constructing Ambient Intelligence - AmI 2007 Workshops, Revised Papers. ed. / Boris Ruyter; Emile Aarts; Manfred Tscheligi; Anind Dey; Hans Gellersen; Bernt Schiele; Alejandro Buchmann; Max Muhlhauser; Erwin Aitenbichler; Reiner Wichert; Alois Ferscha. Springer, 2008. p. 53-62 (Communications in Computer and Information Science; Vol. 11).

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

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T1 - Searching for temporal patterns in AmI sensor data

AU - Tavenard, Romain

AU - Salah, Albert A.

AU - Pauwels, Eric J.

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2008.

PY - 2008

Y1 - 2008

N2 - Anticipation is a key property of human-human communication, and it is highly desirable for ambient environments to have the means of anticipating events to create a feeling of responsiveness and intelligence in the user. In a home or work environment, a great number of low-cost sensors can be deployed to detect simple events: the passing of a person, the usage of an object, the opening of a door. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. Using a testbed that we have developed for this purpose, we first contrast current approaches to the problem. We then extend the best of these approaches, the T-Pattern algorithm, with Gaussian Mixture Models, to obtain a fast and robust algorithm to find patterns in temporal data. Our algorithm can be used to anticipate future events, as well as to detect unexpected events as they occur.

AB - Anticipation is a key property of human-human communication, and it is highly desirable for ambient environments to have the means of anticipating events to create a feeling of responsiveness and intelligence in the user. In a home or work environment, a great number of low-cost sensors can be deployed to detect simple events: the passing of a person, the usage of an object, the opening of a door. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. Using a testbed that we have developed for this purpose, we first contrast current approaches to the problem. We then extend the best of these approaches, the T-Pattern algorithm, with Gaussian Mixture Models, to obtain a fast and robust algorithm to find patterns in temporal data. Our algorithm can be used to anticipate future events, as well as to detect unexpected events as they occur.

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T3 - Communications in Computer and Information Science

SP - 53

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BT - Constructing Ambient Intelligence - AmI 2007 Workshops, Revised Papers

A2 - Ruyter, Boris

A2 - Aarts, Emile

A2 - Tscheligi, Manfred

A2 - Dey, Anind

A2 - Gellersen, Hans

A2 - Schiele, Bernt

A2 - Buchmann, Alejandro

A2 - Muhlhauser, Max

A2 - Aitenbichler, Erwin

A2 - Wichert, Reiner

A2 - Ferscha, Alois

PB - Springer

T2 - European Conference on Ambient Intelligence, AmI 2007

Y2 - 7 November 2007 through 10 November 2007

ER -

Tavenard R, Salah AA, Pauwels EJ. Searching for temporal patterns in AmI sensor data. In Ruyter B, Aarts E, Tscheligi M, Dey A, Gellersen H, Schiele B, Buchmann A, Muhlhauser M, Aitenbichler E, Wichert R, Ferscha A, editors, Constructing Ambient Intelligence - AmI 2007 Workshops, Revised Papers. Springer. 2008. p. 53-62. (Communications in Computer and Information Science). doi: 10.1007/978-3-540-85379-4_7

Searching for temporal patterns in AmI sensor data

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