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Issue No. 02 - April-June (2008 vol. 7)
ISSN: 1536-1268
pp: 32-41
James A. Landay , University of Washington
Predrag "Pedja" Klasnja , University of Washington
Tanzeem Choudhury , Dartmouth College
Gaetano Borriello , University of Washington
Anthony LaMarca , Intel Research
Bruce Hemingway , University of Washington
Jonathan Lester , University of Washington
Sunny Consolvo , Intel Research
Adam Rea , Intel Research
Jeffrey Hightower , Intel Research
Karl Koscher , University of Washington
Louis LeGrand , Intel Research
Ali Rahimi , Intel Research
Dirk Haehnel , Stanford University
Danny Wyatt , University of Washington
Beverly Harrison , Intel Research
The Mobile Sensing Platform (MSP) is a small-form-factor wearable device designed for embedded activity recognition. The MSP aims broadly to support context-aware ubiquitous computing applications. It incorporates multimodal sensing, data processing and inference, storage, all-day battery life, and wireless connectivity into a single 4 oz (115 g) wearable unit. Several design iterations and real-world deployments over the last four years have identified a set of core hardware and software requirements for a mobile inference system. This article presents findings and lessons learned in the course of designing, improving and using this system. This article is part of a special issue on activity-based computing.
activity recognition, embedded systems, machine learning, wearable computers
James A. Landay, Predrag "Pedja" Klasnja, Tanzeem Choudhury, Gaetano Borriello, Anthony LaMarca, Bruce Hemingway, Jonathan Lester, Sunny Consolvo, Adam Rea, Jeffrey Hightower, Karl Koscher, Louis LeGrand, Ali Rahimi, Dirk Haehnel, Danny Wyatt, Beverly Harrison, "The Mobile Sensing Platform: An Embedded Activity Recognition System", IEEE Pervasive Computing, vol. 7, no. , pp. 32-41, April-June 2008, doi:10.1109/MPRV.2008.39
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