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Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

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Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers. / Lukowicz, Paul; Ward, Jamie A; Junker, Holger et al.
2004. 18-22 Paper presented at Pervasive Computing: Proceedings of the 2nd International Conference.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Harvard

Lukowicz, P, Ward, JA, Junker, H, Stäger, M, Tröster, G, Atrash, A & Starner, T 2004, 'Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers', Paper presented at Pervasive Computing: Proceedings of the 2nd International Conference, 1/01/00 pp. 18-22. <http://springerlink.metapress.com/openurl.asp?genre=article&amp;issn=0302-9743&amp;volume=3001&amp;spage=18>

APA

Lukowicz, P., Ward, J. A., Junker, H., Stäger, M., Tröster, G., Atrash, A., & Starner, T. (2004). Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers. 18-22. Paper presented at Pervasive Computing: Proceedings of the 2nd International Conference. http://springerlink.metapress.com/openurl.asp?genre=article&amp;issn=0302-9743&amp;volume=3001&amp;spage=18

Vancouver

Lukowicz P, Ward JA, Junker H, Stäger M, Tröster G, Atrash A et al.. Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers. 2004. Paper presented at Pervasive Computing: Proceedings of the 2nd International Conference.

Author

Lukowicz, Paul ; Ward, Jamie A ; Junker, Holger et al. / Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers. Paper presented at Pervasive Computing: Proceedings of the 2nd International Conference.5 p.

Bibtex

@conference{088564b277184c81a70d7c77c5688511,
title = "Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers",
abstract = "The paper presents a technique to automatically track the progress of maintenance or assembly tasks using body worn sensors. The technique is based on a novel way of combining data from accelerometers with simple frequency matching sound classifcation. This includes the intensity analysis of signals from microphones at different body locations to correlate environmental sounds with user activity. To evaluate our method we apply it to activities in a wood shop. On a simulated assembly task our system can successfully segment and identify most shop activities in a continuous data stream with zero false positives and 84.4% accuracy.",
keywords = "cs_eprint_id, 1628 cs_uid, 382",
author = "Paul Lukowicz and Ward, {Jamie A} and Holger Junker and Mathias St{\"a}ger and Gerhard Tr{\"o}ster and Amin Atrash and Thad Starner",
year = "2004",
month = apr,
language = "English",
pages = "18--22",
note = "Pervasive Computing: Proceedings of the 2nd International Conference ; Conference date: 01-01-1900",

}

RIS

TY - CONF

T1 - Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers

AU - Lukowicz, Paul

AU - Ward, Jamie A

AU - Junker, Holger

AU - Stäger, Mathias

AU - Tröster, Gerhard

AU - Atrash, Amin

AU - Starner, Thad

PY - 2004/4

Y1 - 2004/4

N2 - The paper presents a technique to automatically track the progress of maintenance or assembly tasks using body worn sensors. The technique is based on a novel way of combining data from accelerometers with simple frequency matching sound classifcation. This includes the intensity analysis of signals from microphones at different body locations to correlate environmental sounds with user activity. To evaluate our method we apply it to activities in a wood shop. On a simulated assembly task our system can successfully segment and identify most shop activities in a continuous data stream with zero false positives and 84.4% accuracy.

AB - The paper presents a technique to automatically track the progress of maintenance or assembly tasks using body worn sensors. The technique is based on a novel way of combining data from accelerometers with simple frequency matching sound classifcation. This includes the intensity analysis of signals from microphones at different body locations to correlate environmental sounds with user activity. To evaluate our method we apply it to activities in a wood shop. On a simulated assembly task our system can successfully segment and identify most shop activities in a continuous data stream with zero false positives and 84.4% accuracy.

KW - cs_eprint_id

KW - 1628 cs_uid

KW - 382

M3 - Conference paper

SP - 18

EP - 22

T2 - Pervasive Computing: Proceedings of the 2nd International Conference

Y2 - 1 January 1900

ER -