A computationally light classification method for mobile wellness platforms

Ville Könönen; Jani Mäntyjärvi; Heidi Similä; Juha Pärkkä; Miikka Ermes

doi:10.1109/IEMBS.2008.4649369

A computationally light classification method for mobile wellness platforms

Ville Könönen, Jani Mäntyjärvi, Heidi Similä, Juha Pärkkä, Miikka Ermes

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

The core of activity recognition in mobile wellness devices is a classification engine which maps observations from sensors to estimated classes. There exists a vast number of different classification algorithms that can be used for this purpose in the machine learning literature. Unfortunately, the computational and space requirements of these methods are often too high for the current mobile devices. In this paper we study a simple linear classifier and find, automatically with SFS and SFFS feature selection methods, a suitable set of features to be used with the classification method. The results show that the simple classifier performs comparable to more complex nonlinear k-Nearest Neighbor Classifier. This depicts great potential in implementing the classifier in small mobile wellness devices.

Original language	English
Title of host publication	Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". Vancouver, BC, Canada, 20 - 25 Aug. 2008
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	1167-1170
ISBN (Print)	978-1-4244-1814-5
DOIs	https://doi.org/10.1109/IEMBS.2008.4649369
Publication status	Published - 2008
MoE publication type	A4 Article in a conference publication
Event	30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Personalized Healthcare through Technology - Vancouver, Canada Duration: 20 Aug 2008 → 25 Aug 2008

Conference

Conference	30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Country/Territory	Canada
City	Vancouver
Period	20/08/08 → 25/08/08

Keywords

Wellness platforms
context recognition
pattern classification
activity recognition

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10.1109/IEMBS.2008.4649369

Cite this

Könönen, V., Mäntyjärvi, J., Similä, H., Pärkkä, J., & Ermes, M. (2008). A computationally light classification method for mobile wellness platforms. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". Vancouver, BC, Canada, 20 - 25 Aug. 2008 (pp. 1167-1170). IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/IEMBS.2008.4649369

Könönen, Ville ; Mäntyjärvi, Jani ; Similä, Heidi et al. / A computationally light classification method for mobile wellness platforms. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". Vancouver, BC, Canada, 20 - 25 Aug. 2008. IEEE Institute of Electrical and Electronic Engineers, 2008. pp. 1167-1170

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title = "A computationally light classification method for mobile wellness platforms",

abstract = "The core of activity recognition in mobile wellness devices is a classification engine which maps observations from sensors to estimated classes. There exists a vast number of different classification algorithms that can be used for this purpose in the machine learning literature. Unfortunately, the computational and space requirements of these methods are often too high for the current mobile devices. In this paper we study a simple linear classifier and find, automatically with SFS and SFFS feature selection methods, a suitable set of features to be used with the classification method. The results show that the simple classifier performs comparable to more complex nonlinear k-Nearest Neighbor Classifier. This depicts great potential in implementing the classifier in small mobile wellness devices.",

keywords = "Wellness platforms, context recognition, pattern classification, activity recognition",

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doi = "10.1109/IEMBS.2008.4649369",

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booktitle = "Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - {"}Personalized Healthcare through Technology{"}. Vancouver, BC, Canada, 20 - 25 Aug. 2008",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

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Könönen, V, Mäntyjärvi, J, Similä, H, Pärkkä, J & Ermes, M 2008, A computationally light classification method for mobile wellness platforms. in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". Vancouver, BC, Canada, 20 - 25 Aug. 2008. IEEE Institute of Electrical and Electronic Engineers, pp. 1167-1170, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, British Columbia, Canada, 20/08/08. https://doi.org/10.1109/IEMBS.2008.4649369

A computationally light classification method for mobile wellness platforms. / Könönen, Ville; Mäntyjärvi, Jani; Similä, Heidi et al.
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". Vancouver, BC, Canada, 20 - 25 Aug. 2008. IEEE Institute of Electrical and Electronic Engineers, 2008. p. 1167-1170.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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T1 - A computationally light classification method for mobile wellness platforms

AU - Könönen, Ville

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AU - Similä, Heidi

AU - Pärkkä, Juha

AU - Ermes, Miikka

PY - 2008

Y1 - 2008

N2 - The core of activity recognition in mobile wellness devices is a classification engine which maps observations from sensors to estimated classes. There exists a vast number of different classification algorithms that can be used for this purpose in the machine learning literature. Unfortunately, the computational and space requirements of these methods are often too high for the current mobile devices. In this paper we study a simple linear classifier and find, automatically with SFS and SFFS feature selection methods, a suitable set of features to be used with the classification method. The results show that the simple classifier performs comparable to more complex nonlinear k-Nearest Neighbor Classifier. This depicts great potential in implementing the classifier in small mobile wellness devices.

AB - The core of activity recognition in mobile wellness devices is a classification engine which maps observations from sensors to estimated classes. There exists a vast number of different classification algorithms that can be used for this purpose in the machine learning literature. Unfortunately, the computational and space requirements of these methods are often too high for the current mobile devices. In this paper we study a simple linear classifier and find, automatically with SFS and SFFS feature selection methods, a suitable set of features to be used with the classification method. The results show that the simple classifier performs comparable to more complex nonlinear k-Nearest Neighbor Classifier. This depicts great potential in implementing the classifier in small mobile wellness devices.

KW - Wellness platforms

KW - context recognition

KW - pattern classification

KW - activity recognition

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BT - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". Vancouver, BC, Canada, 20 - 25 Aug. 2008

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Könönen V, Mäntyjärvi J, Similä H, Pärkkä J, Ermes M. A computationally light classification method for mobile wellness platforms. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". Vancouver, BC, Canada, 20 - 25 Aug. 2008. IEEE Institute of Electrical and Electronic Engineers. 2008. p. 1167-1170 doi: 10.1109/IEMBS.2008.4649369

A computationally light classification method for mobile wellness platforms

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