Power and Size Optimized Multi-Sensor Context Recognition Platform

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	Similar Articles Articles by Nagendra B. Bharatula Articles by Mathias Stager Articles by Paul Lukowicz Articles by Gerhard Troster

Ninth IEEE International Symposium on Wearable Computers (ISWC'05)

Osaka, Japan

October 18-October 21

ISBN: 0-7695-2419-2

	ASCII Text	x
	Nagendra B. Bharatula, Mathias Stager, Paul Lukowicz, Gerhard Troster, "Power and Size Optimized Multi-Sensor Context Recognition Platform," Wearable Computers, IEEE International Symposium, pp. 194-195, Ninth IEEE International Symposium on Wearable Computers (ISWC'05), 2005.

	BibTex	x
	@article{ 10.1109/ISWC.2005.42, author = {Nagendra B. Bharatula and Mathias Stager and Paul Lukowicz and Gerhard Troster}, title = {Power and Size Optimized Multi-Sensor Context Recognition Platform}, journal ={Wearable Computers, IEEE International Symposium}, volume = {0}, year = {2005}, isbn = {0-7695-2419-2}, pages = {194-195}, doi = {http://doi.ieeecomputersociety.org/10.1109/ISWC.2005.42}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Wearable Computers, IEEE International Symposium TI - Power and Size Optimized Multi-Sensor Context Recognition Platform SN - 0-7695-2419-2 SP194 EP195 A1 - Nagendra B. Bharatula, A1 - Mathias Stager, A1 - Paul Lukowicz, A1 - Gerhard Troster, PY - 2005 KW - null VL - 0 JA - Wearable Computers, IEEE International Symposium ER -

Nagendra B. Bharatula, Wearable Computing Lab, ETH Z?urich, Switzerland

Mathias Stager, Wearable Computing Lab, ETH Z?urich, Switzerland

Paul Lukowicz, Institute for Computer Systems and Networks, UMIT Hall, Austria

Gerhard Troster, Wearable Computing Lab, ETH Z?urich, Switzerland

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ISWC.2005.42

This paper presents a miniaturized low-power platform for real-time activity recognition. The wearable sensor system comprises of accelerometers, a microphone, a light sensor and signal processing units. The recognition is performed with low-power features and a decision tree classi- fier. Power measurements show that our 4.15?2.75 cm2, 9 gram platform consumes less than 3mW and can perform continuous classification and result transmission for 129 hours on a small lithium-polymer battery.

Citation:

Nagendra B. Bharatula, Mathias Stager, Paul Lukowicz, Gerhard Troster, "Power and Size Optimized Multi-Sensor Context Recognition Platform," iswc, pp.194-195, Ninth IEEE International Symposium on Wearable Computers (ISWC'05), 2005

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