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Issue No.02 - April-June (2008 vol.7)
pp: 32-41
Gaetano Borriello , University of Washington
Sunny Consolvo , Intel Research
Dirk Haehnel , Stanford University
Beverly Harrison , Intel Research
Bruce Hemingway , University of Washington
Jeffrey Hightower , Intel Research
Predrag "Pedja" Klasnja , University of Washington
Karl Koscher , University of Washington
Anthony LaMarca , Intel Research
James A. Landay , University of Washington
Louis LeGrand , Intel Research
Jonathan Lester , University of Washington
Ali Rahimi , Intel Research
Adam Rea , Intel Research
Danny Wyatt , University of Washington
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
Gaetano Borriello, Sunny Consolvo, Dirk Haehnel, Beverly Harrison, Bruce Hemingway, Jeffrey Hightower, Predrag "Pedja" Klasnja, Karl Koscher, Anthony LaMarca, James A. Landay, Louis LeGrand, Jonathan Lester, Ali Rahimi, Adam Rea, Danny Wyatt, "The Mobile Sensing Platform: An Embedded Activity Recognition System", IEEE Pervasive Computing, vol.7, no. 2, pp. 32-41, April-June 2008, doi:10.1109/MPRV.2008.39
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