@article{ChoudhuryBorrielloEtAl08IEEEpervasive,
title = {The Mobile Sensing Platform: An Embedded Activity Recognition System},
author = {Tanzeem Choudhury and Gaetano Borriello and Sunny Consolvo and Dirk Haehnel and Beverly Harrison and Bruce Hemingway and Jeffrey Hightower and Predrag Klasnja and Karl Koscher and Anthony LaMarca and James A. Landay and Louis LeGrand and Jonathan Lester and Ali Rahimi and Adam Rea and Danny Wyatt},
journal = {Pervasive Computing},
number = {2},
pages = {32-41},
url = {http://dx.doi.org/10.1109/MPRV.2008.39},
volume = {7},
year = {2008},
abstract = {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.},
issn = {1536-1268}, timestamp = {2008.06.02}, file = {IEEE Digital Library:2008/ChoudhuryBorrielloEtAl08IEEEpervasive.pdf:PDF}, owner = {flint},
keywords = {ai data device embedded ieee multimodal paper processing sensor v0805 }
}
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