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@INPROCEEDINGS{Liao2007a,
  author = {Lin Liao and Tanzeem Choudhury and Dieter Fox and Henry A. Kautz},
  title = {Training Conditional Random Fields Using Virtual Evidence Boosting},
  booktitle = {IJCAI},
  year = {2007},
  pages = {2530-2535},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  crossref = {DBLP:conf/ijcai/2007},
  ee = {http://dli.iiit.ac.in/ijcai/IJCAI-2007/PDF/IJCAI07-407.pdf},
  file = {Liao2007a.pdf:Liao2007a.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.03}
}

@INPROCEEDINGS{Mahdaviani2007,
  author = {Maryam Mahdaviani and Tanzeem Choudhury},
  title = {Fast and Scalable Training of Semi-Supervised CRFs with Application
	to Activity Recognition},
  booktitle = {NIPS},
  year = {2007},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  crossref = {DBLP:conf/nips/2007},
  ee = {http://books.nips.cc/papers/files/nips20/NIPS2007_0863.pdf},
  file = {Mahdaviani2007.pdf:Mahdaviani2007.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.03}
}

@MISC{Abowd1997,
  author = {Gregory D. Abowd and Christopher G. Atkeson and Jason Hong and Sue
	Long and Rob Kooper},
  title = {Cyberguide: A Mobile Context-Aware Tour Guide},
  year = {1997},
  citeseercitationcount = {0},
  citeseerurl = {http://citeseer.ist.psu.edu/36540.html},
  comment = {Not relevant.},
  file = {Abowd1997.pdf:Abowd1997.pdf:PDF},
  owner = {chris},
  pdf = {Abowd1997.pdf},
  timestamp = {2009.12.01}
}

@ARTICLE{Aha1991,
  author = {Aha, David W. and Kibler, Dennis and Albert, Marc K.},
  title = {Instance-Based Learning Algorithms},
  journal = {Machine Learning},
  year = {1991},
  volume = {6},
  pages = {37--66},
  number = {1},
  month = {January},
  abstract = {Storing and using specific instances improves the performance of several
	supervised learning algorithms. These include algorithms that learn
	decision trees, classification rules, and distributed networks. However,
	no investigation has analyzed algorithms that use only specific instances
	to solve incremental learning tasks. In this paper, we describe a
	framework and methodology, called instance-based learning, that generates
	classification predictions using only specific instances. Instance-based
	learning algorithms do not maintain a set of abstractions derived
	from specific instances. This approach extends the nearest neighbor
	algorithm, which has large storage requirements. We describe how
	storage requirements can be significantly reduced with, at most,
	minor sacrifices in learning rate and classification accuracy. While
	the storage-reducing algorithm performs well on several real-world
	databases, its performance degrades rapidly with the level of attribute
	noise in training instances. Therefore, we extended it with a significance
	test to distinguish noisy instances. This extended algorithm's performance
	degrades gracefully with increasing noise levels and compares favorably
	with a noise-tolerant decision tree algorithm.},
  address = {Hingham, MA, USA},
  citeulike-article-id = {1527614},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=104717},
  citeulike-linkout-1 = {http://dx.doi.org/10.1023/A:1022689900470},
  citeulike-linkout-2 = {http://www.springerlink.com/content/kn127378pg361187},
  day = {1},
  doi = {10.1023/A:1022689900470},
  file = {Aha1991.pdf:Aha1991.pdf:PDF},
  issn = {0885-6125},
  keywords = {learning},
  owner = {chris},
  posted-at = {2007-08-01 14:37:32},
  priority = {4},
  publisher = {Kluwer Academic Publishers},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1023/A:1022689900470}
}

@ARTICLE{Bannach2008,
  author = {Bannach, D. and Lukowicz, P. and Amft, O.},
  title = {Rapid Prototyping of Activity Recognition Applications},
  journal = IEEE_M_PVC,
  year = {2008},
  volume = {7},
  pages = {22--31},
  number = {2},
  month = {April--June },
  comment = {The concept of the CRN Toolbox stems from the observation that most
	activity recognition systems rely on a relatively small set of algorithms.
	These include sliding-window signal partitioning, standard time and
	frequency domain features, classifiers, and time series or event-based
	modeling algorithms.},
  doi = {10.1109/MPRV.2008.36},
  file = {Bannach2008.pdf:Bannach2008.pdf:PDF},
  owner = {chris},
  pdf = {Bannach2008.pdf},
  timestamp = {2009.12.01}
}

@ARTICLE{Bao2004,
  author = {Bao, L. and Intille, S. S.},
  title = {Activity Recognition from User-Annotated Acceleration Data},
  journal = {Pervasive Computing},
  year = {2004},
  volume = {3001},
  pages = {1--17},
  abstract = {In this work, algorithms are developed and evaluated to detect physical
	activities from data acquired using five small biaxial accelerometers
	worn simultaneously on different parts of the body. Acceleration
	data was collected from 20 subjects without researcher supervision
	or observation. Subjects were asked to perform a sequence of everyday
	tasks but not told specifically where or how to do them. Mean, energy,
	frequency-domain entropy, and correlation of acceleration data was
	calculated and several classifiers using these features were tested.
	Decision tree classifiers showed the best performance recognizing
	everyday activities with an overall accuracy rate of 84\%. The results
	show that although some activities are recognized well with subject-independent
	training data, others appear to require subject-specific training
	data. The results suggest that multiple accelerometers aid in recognition
	because conjunctions in acceleration feature values can effectively
	discriminate many activities. With just two biaxial accelerometers
	– thigh and wrist – the recognition performance dropped only slightly.
	This is the first work to investigate performance of recognition
	algorithms with multiple, wire-free accelerometers on 20 activities
	using datasets annotated by the subjects themselves.},
  citeulike-article-id = {1188357},
  citeulike-linkout-0 = {http://www.springerlink.com/content/9aqflyk4f47khyjd},
  comment = {Multiple accelerometers in different locations. Decision-tree classifiers.
	Some activities are subject-specific.
	
	For instance, laboratory acceleration data of walking displays distinct
	phases of a consistent gait cycle which can aide recognition of pace
	and incline [2]. However, acceleration data from the same subject
	outside of the laboratory may display marked fluctuation in the relation
	of gait phases and total gait length due to decreased self-awareness
	and fluctuations in traffic.},
  file = {Bao2004.pdf:Bao2004.pdf:PDF},
  keywords = {acceleration},
  owner = {chris},
  posted-at = {2008-04-16 11:42:22},
  priority = {4},
  timestamp = {2009.12.06},
  url = {http://www.springerlink.com/content/9aqflyk4f47khyjd}
}

@INCOLLECTION{Bardram2005,
  author = {Bardram, Jakob E.},
  title = {The Java Context Awareness Framework (JCAF) - A Service Infrastructure
	and Programming Framework for Context-Aware Applications},
  booktitle = {Pervasive Computing},
  publisher = {IEEE},
  year = {2005},
  pages = {98--115},
  abstract = {Context-awareness is a key concept in ubiquitous computing. But to
	avoid developing dedicated context-awareness sub-systems for specific
	application areas there is a need for more generic programming frameworks.
	Such frameworks can help the programmer develop and deploy context-aware
	applications faster. This paper describes the Java Context-Awareness
	Framework – JCAF, which is a Java-based context-awareness infrastructure
	and programming API for creating context-aware computer applications.
	The paper presents the design goals of JCAF, its runtime architecture,
	and its programming model. The paper presents some applications of
	using JCAF in three different applications and discusses lessons
	learned from using JCAF.},
  citeulike-article-id = {1145979},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/11428572_7},
  citeulike-linkout-1 = {http://www.springerlink.com/content/yl2fen8clqqwq2tb},
  doi = {10.1007/11428572_7},
  file = {Bardram2005.pdf:Bardram2005.pdf:PDF},
  journal = {Pervasive Computing},
  keywords = {awareness, context, framework},
  owner = {chris},
  posted-at = {2007-06-26 09:54:05},
  priority = {4},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1007/11428572_7}
}

@ARTICLE{Bellavista2008,
  author = {Bellavista, P. and Kupper, A. and Helal, S.},
  title = {Location-Based Services: Back to the Future},
  journal = IEEE_M_PVC,
  year = {2008},
  volume = {7},
  pages = {85--89},
  number = {2},
  month = {April--June },
  comment = {Android mail milestone in LBSs, LBS originated with E911 mandate.
	Moving LSBs from operator controlled to user controlled major factor
	in success, helped by open systems such as Android and Openmoko.},
  doi = {10.1109/MPRV.2008.34},
  file = {Bellavista2008.pdf:Bellavista2008.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@INPROCEEDINGS{Bellotti2008,
  author = {Bellotti, Victoria and Begole, Bo and Chi, Ed H. and Ducheneaut,
	Nicolas and Fang, Ji and Isaacs, Ellen and King, Tracy and Newman,
	Mark W. and Partridge, Kurt and Price, Bob and Rasmussen, Paul and
	Roberts, Michael and Schiano, Diane J. and Walendowski, Alan},
  title = {Activity-based serendipitous recommendations with the Magitti mobile
	leisure guide},
  booktitle = {CHI '08: Proceeding of the twenty-sixth annual SIGCHI conference
	on Human factors in computing systems},
  year = {2008},
  pages = {1157--1166},
  address = {New York, NY, USA},
  publisher = {ACM},
  citeulike-article-id = {2859755},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1357237},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/1357054.1357237},
  doi = {10.1145/1357054.1357237},
  file = {Bellotti2008.pdf:Bellotti2008.pdf:PDF},
  isbn = {9781605580111},
  keywords = {ibm, iphone, triage},
  owner = {chris},
  posted-at = {2008-06-03 19:57:41},
  priority = {5},
  timestamp = {2009.12.03},
  url = {http://dx.doi.org/10.1145/1357054.1357237}
}

@INPROCEEDINGS{Caros2005,
  author = {Car\'os, JS. and Ch\'etelat, O. and Celka, P. and Dasen, S.},
  title = {Very low complexity algorithm for ambulatory activity classification},
  booktitle = {European Medical \& Biological Engineering Conference and IFMBE European
	Conference on Biomedical Engineering},
  year = {2005},
  comment = {Mechanism of walking is defined as controlled falling, where the centre
	of gravity oscillates over the supporting limb following an inverted
	pendulum movement. At the end of each pendulum movement, the strike
	of the heel on the floor deaccelerates the swinging phase by generating
	an abrupt vertical acceleration. *Discrete time index, discrete time
	Dirac distribution*. Double integration - walking/stairs.},
  file = {Caros2005.pdf:Caros2005.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@ARTICLE{Choudhury2008,
  author = {Choudhury, T. and Consolvo, S. and Harrison, B. and Hightower, J.
	and LaMarca, A. and LeGrand, L. and Rahimi, A. and Rea, A. and Bordello,
	G. and Hemingway, B. and Klasnja, P. and Koscher, K. and Landay,
	J.A. and Lester, J. and Wyatt, D. and Haehnel, D.},
  title = {The Mobile Sensing Platform: An Embedded Activity Recognition System},
  journal = IEEE_M_PVC,
  year = {2008},
  volume = {7},
  pages = {32--41},
  number = {2},
  month = {April--June },
  comment = {Requirements, general architecture, privacy - audio. Structured prediction
	- temporal/structure & activity/context dependencies. Difficulties
	with training (labelling large amount of data); semi-supervised training},
  doi = {10.1109/MPRV.2008.39},
  file = {Choudhury2008.pdf:Choudhury2008.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@INPROCEEDINGS{Consolvo2008,
  author = {Consolvo, Sunny and Mcdonald, David W. and Toscos, Tammy and Chen,
	Mike Y. and Froehlich, Jon and Harrison, Beverly and Klasnja, Predrag
	and Lamarca, Anthony and Legrand, Louis and Libby, Ryan and Smith,
	Ian and Landay, James A.},
  title = {Activity sensing in the wild: a field trial of ubifit garden},
  booktitle = {CHI '08: Proceeding of the twenty-sixth annual SIGCHI conference
	on Human factors in computing systems},
  year = {2008},
  pages = {1797--1806},
  address = {New York, NY, USA},
  publisher = {ACM},
  citeulike-article-id = {2977124},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1357054.1357335},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/1357054.1357335},
  doi = {10.1145/1357054.1357335},
  file = {Consolvo2008.pdf:Consolvo2008.pdf:PDF},
  isbn = {9781605580111},
  keywords = {sensing, ubiquitous},
  location = {Florence, Italy},
  owner = {chris},
  posted-at = {2008-08-25 01:26:32},
  priority = {2},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1145/1357054.1357335}
}

@ARTICLE{Davies2008,
  author = {Davies, Nigel and Siewiorek, Daniel P. and Sukthankar, Rahul},
  title = {Activity-Based Computing},
  journal = IEEE_M_PVC,
  year = {2008},
  volume = {7},
  pages = {20--21},
  number = {2},
  comment = {General intro and basic history},
  doi = {10.1109/MPRV.2008.26},
  file = {Davies2008.pdf:Davies2008.pdf:PDF},
  issn = {1536-1268},
  keywords = {activity recognition, activity-based computing, context-aware computing},
  owner = {chris},
  pdf = {Davies2008.pdf},
  timestamp = {2009.11.30}
}

@ARTICLE{Serugendo2008,
  author = {Di Marzo Serugendo, G.},
  title = {Activity-Based Computing},
  journal = IEEE_M_PVC,
  year = {2008},
  volume = {7},
  pages = {58--61},
  number = {2},
  month = {April--June },
  comment = {Not relevant.},
  doi = {10.1109/MPRV.2008.25},
  file = {Serugendo2008.pdf:Serugendo2008.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@INPROCEEDINGS{Dornbush2005,
  author = {Dornbush, S. and Fisher, K. and McKay, K. and Prikhodko, A. and Segall,
	Z.},
  title = {XPOD - A Human Activity and Emotion Aware Mobile Music Player},
  booktitle = {Proc. 2nd International Conference on Mobile Technology, Applications
	and Systems},
  year = {2005},
  pages = {1--6},
  comment = {We used classifiers from the open source Weka library[Witten and Frank,
	2005] and neural networks from the open source Joone library[Marrone
	and Team, 2006]. Decision Tree (J48) [Quinlan, 1993] 41% acc. AdaBoost
	[Freund and Schapire, 1996] 46% acc. Support Vector Machine (SVM)
	[Platt, 1998; Keerthi et al 2001] 43%. K-Nearest Neighbours [Aha
	and Kibler, 1991] 47% acc. Neural networks 43% acc.},
  doi = {10.1109/MTAS.2005.207159},
  file = {Dornbush2005.pdf:Dornbush2005.pdf:PDF},
  keywords = {audio equipment, humanities, mobile handsets, XPod, emotion aware
	mobile music player, human activity, mobile MP3 player, mobile devices,
	mobile phone user experience},
  owner = {chris},
  timestamp = {2009.12.01}
}

@MISC{Eagle2004,
  author = {Eagle, N. and Pentland, A.},
  title = {Mobile Matchmaking: Proximity Sensing and Cueing},
  year = {2004},
  comment = {Bluetooth battery life; relationship type based on time of day and
	bluetooth density; Gaussian mixture model 90%, SVM better?},
  file = {Eagle2004.pdf:Eagle2004.pdf:PDF},
  journal = {IEEE Pervasive, Special Issue on Smart Phones},
  owner = {chris},
  timestamp = {2009.12.01}
}

@ARTICLE{Eagle2006,
  author = {Eagle, Nathan and Sandy Pentland, Alex},
  title = {Reality mining: sensing complex social systems},
  journal = {Personal and Ubiquitous Computing},
  year = {2006},
  volume = {10},
  pages = {255--268},
  number = {4},
  month = {May},
  abstract = {Abstract\&nbsp;\&nbsp;We introduce a system for sensing complex social
	systems with data collected from 100 mobile phones over the course
	of 9\&nbsp;months. We demonstrate the ability to use standard Bluetooth-enabled
	mobile telephones to measure information access and use in different
	contexts, recognize social patterns in daily user activity, infer
	relationships, identify socially significant locations, and model
	organizational rhythms.},
  address = {London, UK},
  citeulike-article-id = {899208},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1122739.1122745},
  citeulike-linkout-1 = {http://dx.doi.org/10.1007/s00779-005-0046-3},
  citeulike-linkout-2 = {http://www.springerlink.com/content/l562745318077t54},
  day = {1},
  doi = {10.1007/s00779-005-0046-3},
  file = {Eagle2006.pdf:Eagle2006.pdf:PDF},
  issn = {1617-4909},
  keywords = {complex, mining, reality, sensing, social, systems},
  owner = {chris},
  posted-at = {2009-10-12 11:59:33},
  priority = {2},
  publisher = {Springer-Verlag},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1007/s00779-005-0046-3}
}

@MISC{Floreen2008,
  author = {Patrik Floréen and Joonas Kukkonen and Eemil Lagerspetz and Petteri
	Nurmi and Jukka Suomela},
  title = {BeTelGeuse: Tool for Context Data Gathering via Bluetooth},
  year = {2008},
  comment = {Not relevant.},
  file = {Floreen2008.pdf:Floreen2008.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@MASTERSTHESIS{Garakani2009,
  author = {AB Garakani},
  title = {Real-Time Classification of Everyday Fitness Activities on Windows
	Mobile},
  school = {University of Washington},
  year = {2009},
  comment = {Instant/smooth classification ideas. Discrete fourier transform on
	accelerometer data. 24 features per axis. Naive bayes model. 25Hz
	accelerometer sampling battery life - 7 days down to 24 hours.},
  file = {Garakani2009.pdf:Garakani2009.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@INBOOK{Han2006,
  chapter = {6},
  pages = {348-350},
  title = {Data mining: concepts and techniques},
  publisher = {Morgan Kaufmann},
  year = {2006},
  author = {Han, J and Kamber, M},
  owner = {chris},
  timestamp = {2010.01.14}
}

@MISC{Hein2008,
  author = {Albert Hein and Thomas Kirste},
  title = {Towards Recognizing Abstract Activities: An Unsupervised Approach},
  year = {2008},
   = {http://www.scientificcommons.org/48837277},
  abstract = {Abstract. The recognition of abstract high-level activities using
	wearable sensors is an important prerequisite for context aware mobile
	assistance, especially in AAL and medical care applications. A major
	difficulty in detecting this type of activities is that different
	activities often share similar motion patterns. One possible solution
	is to aggregate these activities from shorter, easier to detect base
	level actions, but the explicit annotation of these is not trivial
	and very time consuming. In this paper we introduce a simple clustering
	based method for the recognition of compound activities at a high
	level of abstraction using k-Means as an unsupervised learning algorithm.
	A general problem of these methods is that the resulting cluster
	affiliations are typically not human readable and some kind of interpretation
	is needed. To achieve this, we developed a hybrid approach using
	a generative probabilistic model built on top of the clusterer. We
	adapted a Hidden Markov Model for mapping the cluster memberships
	onto high-level activities and sucessfully evaluated the feasibility
	of this technique using experimental data from two test runs of a
	home care scenario showing a higher accuracy and robustness than
	conventional discriminative methods.},
  comment = {Unsupervised learning for basic actions, then overlying hiden markov
	model to classify into higher-level activities. K-means clustering
	algorithm for identification and detection of base-level motion patterns.},
  file = {Hein2008.pdf:Hein2008.pdf:PDF},
  institution = {CiteSeerX - Scientific Literature Digital Library and Search Engine
	[http://citeseerx.ist.psu.edu/oai2] (United States)},
  keywords = {High-Level Activities, Clustering, Probabilistic Models, AAL},
  owner = {chris},
  timestamp = {2009.12.01},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.6671}
}

@INPROCEEDINGS{Hightower2005,
  author = {Hightower, Jeffrey and Consolvo, Sunny and Lamarca, Anthony and Smith,
	Ian and Hughes, Jeff},
  title = {Learning and Recognizing the Places We Go},
  booktitle = {UbiComp 2005: Ubiquitous Computing},
  year = {2005},
  pages = {159--176},
  abstract = {Location-enhanced mobile devices are becoming common, but applications
	built for these devices find themselves suffering a mismatch between
	the latitude and longitude that location sensors provide and the
	colloquial place label that applications need. Conveying my location
	to my spouse, for example as (48.13641N, 11.57471E), is less informative
	than saying "at home". We introduce an algorithm called BeaconPrint
	that uses WiFi and GSM radio fingerprints collected by someone's
	personal mobile device to automatically learn the places they go
	and then detect when they return to those places. BeaconPrint does
	not automatically assign names or semantics to places. Rather, it
	provides the technological foundation to support this task. We compare
	BeaconPrint to three existing algorithms using month-long trace logs
	from each of three people. Algorithmic results are supplemented with
	a survey study about the places people go. BeaconPrint is over 90\%
	accurate in learning and recognizing places. Additionally, it improves
	accuracy in recognizing places visited infrequently or for short
	durations - a category where previous approaches have fared poorly.
	BeaconPrint demonstrates 63\% accuracy for places someone returns
	to only once or visits for less than 10 minutes, increasing to 80\%
	accuracy for places visited twice.},
  citeulike-article-id = {1382076},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/11551201_10},
  comment = {K-means clustering. 9.6 minute windows. Most algorithms rely on GPS
	blackouts or Wifi beacons.},
  doi = {10.1007/11551201_10},
  file = {Hightower2005.pdf:Hightower2005.pdf:PDF},
  journal = {UbiComp 2005: Ubiquitous Computing},
  keywords = {learning, location, places, prediction, significant},
  owner = {chris},
  posted-at = {2009-03-24 14:34:56},
  priority = {0},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1007/11551201_10}
}

@INPROCEEDINGS{Horvitz1998,
  author = {Horvitz, E. and Breese, J. and Heckerman, D. and Hovel, D. and Rommelse,
	K.},
  title = {The Lumiere project: Bayesian user modeling for inferring the goals
	and needs of software users},
  booktitle = {In Proceedings of the Fourteenth Conference on Uncertainty in Artificial
	Intelligence},
  year = {1998},
  pages = {256--265},
  address = {Madison, WI},
  month = {July},
  abstract = {The Lumi`ere Project centers on harnessing probability and utility
	to provide assistance to computer software users. We review work
	on Bayesian user models that can be employed to infer a user's needs
	by considering a user's background, actions, and queries. Several
	problems were tackled in Lumi`ere research, including (1) the construction
	of Bayesian models for reasoning about the time-varying goals of
	computer users from their observed actions and queries, (2) gaining
	access to a stream of...},
  citeulike-article-id = {1269522},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.39.8472},
  file = {Horvitz1998.pdf:Horvitz1998.pdf:PDF},
  keywords = {bayesian, computing, inference, modeling, user},
  owner = {chris},
  posted-at = {2007-05-14 23:50:35},
  priority = {4},
  timestamp = {2009.12.06},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.39.8472}
}

@INPROCEEDINGS{Hudson2003,
  author = {Hudson, Scott and Fogarty, James and Atkeson, Christopher and Avrahami,
	Daniel and Forlizzi, Jodi and Kiesler, Sara and Lee, Johnny and Yang,
	Jie},
  title = {Predicting human interruptibility with sensors: a Wizard of Oz feasibility
	study},
  booktitle = {CHI '03: Proceedings of the SIGCHI conference on Human factors in
	computing systems},
  year = {2003},
  pages = {257--264},
  publisher = {ACM Press},
   = {New York, NY, USA},
  citeulike-article-id = {410042},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=642657},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/642611.642657},
  doi = {10.1145/642611.642657},
  file = {Hudson2003.pdf:Hudson2003.pdf:PDF},
  isbn = {1581136307},
  keywords = {interruptibility, wizard\_of\_oz},
  owner = {chris},
  posted-at = {2005-12-04 00:50:45},
  priority = {0},
  timestamp = {2010.01.11},
  url = {http://dx.doi.org/10.1145/642611.642657}
}

@INPROCEEDINGS{Huynh2005,
  author = {Huynh, T\^{a}m and Schiele, Bernt},
  title = {Analyzing features for activity recognition},
  booktitle = {sOc-EUSAI '05: Proceedings of the 2005 joint conference on Smart
	objects and ambient intelligence},
  year = {2005},
  pages = {159--163},
  address = {New York, NY, USA},
  publisher = {ACM},
  abstract = {Human activity is one of the most important ingredients of context
	information. In wearable computing scenarios, activities such as
	walking, standing and sitting can be inferred from data provided
	by body-worn acceleration sensors. In such settings, most approaches
	use a single set of features, regardless of which activity to be
	recognized. In this paper we show that recognition rates can be improved
	by careful selection of individual features for each activity. We
	present a systematic analysis of features computed from a real-world
	data set and show how the choice of feature and the window length
	over which the feature is computed affects the recognition rates
	for different activities. Finally, we give a recommendation of suitable
	features and window lengths for a set of common activities.},
  citeulike-article-id = {1076182},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1107591},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/1107548.1107591},
  comment = {Better recognition rates when selecting features based on activity.
	Popular features: mean, standard deviation, energy, entropy, correleation
	between axis, discrete FFT coefficients. FFT features generally best,
	but different coefficients and windows for each activity.},
  doi = {10.1145/1107548.1107591},
  file = {Huynh2005.pdf:Huynh2005.pdf:PDF},
  isbn = {1-59593-304-2},
  keywords = {activity, recognition},
  location = {Grenoble, France},
  owner = {chris},
  posted-at = {2007-03-17 06:17:32},
  priority = {2},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1145/1107548.1107591}
}

@MISC{Keerthi1999,
  author = {Keerthi, S. and Shevade, S. and Bhattacharyya, C. and Murthy, K.},
  title = {Improvements to Platt's SMO algorithm for SVM classifier design},
  year = {1999},
  abstract = {This paper points out an important source of confusion and ineciency
	in Platt's Sequential
	
	Minimal Optimization (SMO) algorithm that is caused by the use of
	a single threshold value.
	
	Using clues from the KKT conditions for the dual problem, two threshold
	parameters are employed
	
	to derive modications of SMO. These modied algorithms perform signicantly
	faster
	
	than the original SMO on all benchmark datasets tried.
	
	1 Introduction
	
	In the past few years, there has been a lot of excitement...},
  citeulike-article-id = {1772853},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.8538},
  file = {Keerthi1999.pdf:Keerthi1999.pdf:PDF},
  keywords = {smo, svm},
  owner = {chris},
  posted-at = {2008-04-12 20:18:51},
  priority = {2},
  timestamp = {2009.12.06},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.8538}
}

@INPROCEEDINGS{Lee2007,
  author = {Jae Young Lee and Hoff, W.},
  title = {Activity Identification Utilizing Data Mining Techniques},
  booktitle = {Proc. IEEE Workshop on Motion and Video Computing WMVC '07},
  year = {2007},
  pages = {12--12},
  month = {Feb. },
  doi = {10.1109/WMVC.2007.4},
  file = {Lee2007.pdf:Lee2007.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.03}
}

@INPROCEEDINGS{Lester2006,
  author = {Lester, Jonathan and Choudhury, Tanzeem and Borriello, Gaetano},
  title = {A Practical Approach to Recognizing Physical Activities},
  booktitle = {Pervasive Computing},
  year = {2006},
  pages = {1--16},
   = {We are developing a personal activity recognition system that is practical,
	reliable, and can be incorporated into a variety of health-care related
	applications ranging from personal fitness to elder care. To make
	our system appealing and useful, we require it to have the following
	properties: (i) data only from a single body location needed, and
	it is not required to be from the same point for every user; (ii)
	should work out of the box across individuals, with personalization
	only enhancing its recognition abilities; and (iii) should be effective
	even with a cost-sensitive subset of the sensors and data features.
	In this paper, we present an approach to building a system that exhibits
	these properties and provide evidence based on data for 8 different
	activities collected from 12 different subjects. Our results indicate
	that the system has an accuracy rate of approximately 90\% while
	meeting our requirements. We are now developing a fully embedded
	version of our system based on a cell-phone platform augmented with
	a Bluetooth-connected sensor board.},
  citeulike-article-id = {997656},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/11748625_1},
  citeulike-linkout-1 = {http://www.springerlink.com/content/7048888592382352},
  doi = {10.1007/11748625_1},
  file = {Lester2006.pdf:Lester2006.pdf:PDF},
  journal = {Pervasive Computing},
  keywords = {accelerometer, activity-recognition},
  owner = {chris},
  posted-at = {2007-10-12 10:21:05},
  priority = {2},
  timestamp = {2009.12.03},
  url = {http://dx.doi.org/10.1007/11748625_1}
}

@INPROCEEDINGS{Lester2005,
  author = {Lester, Jonathan and Choudhury, Tanzeem and Kern, Nicky and Borriello,
	Gaetano and Hannaford, Blake},
  title = {A hybrid discriminative/generative approach for modeling human activities},
  booktitle = {In Proc. of the International Joint Conference on Artificial Intelligence
	(IJCAI},
  year = {2005},
  pages = {766--772},
  abstract = {Accurate recognition and tracking of human activities is an important
	goal of ubiquitous computing. Recent advances in the development
	of multi-modal wearable sensors enable us to gather rich datasets
	of human activities. However, the problem of automatically identifying
	the most useful features for modeling such activities remains largely
	unsolved. In this paper we present a hybrid approach to recognizing
	activities, which combines boosting to discriminatively select useful
	features and learn an ensemble of static classifiers to recognize
	different activities, with hidden Markov models (HMMs) to capture
	the temporal regularities and smoothness of activities. We tested
	the activity recognition system using over 12 hours of wearable-sensor
	data collected by volunteers in natural unconstrained environments.
	The models succeeded in identifying a small set of maximally informative
	features, and were able identify ten different human activities with
	an accuracy of 95\%. 1},
  citeulike-article-id = {3291348},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.5776},
  file = {Lester2005.pdf:Lester2005.pdf:PDF},
  keywords = {transitgenie},
  owner = {chris},
  posted-at = {2009-07-11 19:30:29},
  priority = {4},
  timestamp = {2009.12.06},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.5776}
}

@ARTICLE{Liao2007,
  author = {Liao, Lin and Fox, Dieter and Kautz, Henry},
  title = {Extracting Places and Activities from GPS Traces Using Hierarchical
	Conditional Random Fields},
  journal = {Int. J. Rob. Res.},
  year = {2007},
  volume = {26},
  pages = {119--134},
  number = {1},
  abstract = {Learning patterns of human behavior from sensor data is extremely
	important for high-level activity inference. This paper describes
	how to extract a person's activities and significant places from
	traces of GPS data. The system uses hierarchically structured conditional
	random fields to generate a consistent model of a person's activities
	and places. In contrast to existing techniques, this approach takes
	the high-level context into account in order to detect the significant
	places of a person. Experiments show significant improvements over
	existing techniques. Furthermore, they indicate that the proposed
	system is able to robustly estimate a person's activities using a
	model that is trained from data collected by other persons.},
  address = {Thousand Oaks, CA, USA},
  citeulike-article-id = {3480910},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1229555.1229562},
  citeulike-linkout-1 = {http://dx.doi.org/10.1177/0278364907073775},
  doi = {10.1177/0278364907073775},
  file = {Liao2007.pdf:Liao2007.pdf:PDF},
  issn = {0278-3649},
  keywords = {activity-prediction, place-finding},
  owner = {chris},
  posted-at = {2008-11-04 21:40:32},
  priority = {2},
  publisher = {Sage Publications, Inc.},
  timestamp = {2009.12.03},
  url = {http://dx.doi.org/10.1177/0278364907073775}
}

@ARTICLE{Liao2007b,
  author = {Liao, Lin and Patterson, Donald J. and Fox, Dieter and Kautz, Henry},
  title = {Learning and inferring transportation routines},
  journal = {Artificial Intelligence},
  year = {2007},
  volume = {171},
  pages = {311--331},
  number = {5-6},
  month = {April},
   = {This paper introduces a hierarchical Markov model that can learn and
	infer a user's daily movements through an urban community. The model
	uses multiple levels of abstraction in order to bridge the gap between
	raw GPS sensor measurements and high level information such as a
	user's destination and mode of transportation. To achieve efficient
	inference, we apply Rao-Blackwellized particle filters at multiple
	levels of the model hierarchy. Locations such as bus stops and parking
	lots, where the user frequently changes mode of transportation, are
	learned from GPS data logs without manual labeling of training data.
	We experimentally demonstrate how to accurately detect novel behavior
	or user errors (e.g. taking a wrong bus) by explicitly modeling activities
	in the context of the user's historical data. Finally, we discuss
	an application called "Opportunity Knocks" that employs our techniques
	to help cognitively-impaired people use public transportation safely.},
  citeulike-article-id = {1541495},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1238288},
  citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.artint.2007.01.006},
  citeulike-linkout-2 = {http://www.sciencedirect.com/science/article/B6TYF-4N49VP9-1/2/8f05b8caf7327ceb8762ab5e1b95efc9},
  doi = {10.1016/j.artint.2007.01.006},
  file = {Liao2007b.pdf:Liao2007b.pdf:PDF},
  keywords = {learning, statistical-inference},
  owner = {chris},
  posted-at = {2008-11-28 02:40:32},
  priority = {2},
  timestamp = {2010.01.13},
  url = {http://dx.doi.org/10.1016/j.artint.2007.01.006}
}

@ARTICLE{Lukowicz2002,
  author = {Lukowicz, P. and Junker, H. and St\"{a}ger, M. and von B\"{u}ren,
	T. and Tr\"{o}ster, G.},
  title = {WearNET: A Distributed Multi-sensor System for Context Aware Wearables},
  journal = {UbiComp 2002: Ubiquitous Computing},
  year = {2002},
  volume = {1},
  pages = {361--370},
  abstract = {This paper describes a distributed, multi-sensor system architecture
	designed to provide a wearable computer with a wide range of complex
	context information. Starting from an analysis of useful high level
	context information we present a top down design that focuses on
	the peculiarities of wearable applications. Thus, our design devotes
	particular attention to sensor placement, system partitioning as
	well as resource requirements given by the power consumption, computational
	intensity and communication overhead. We describe an implementation
	of our architecture and initial experimental results obtained with
	the system.},
  citeulike-article-id = {3909016},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/3-540-45809-3_28},
  citeulike-linkout-1 = {http://www.springerlink.com/content/kky208rx9e98m0xg},
  doi = {10.1007/3-540-45809-3_28},
  file = {Lukowicz2002.pdf:Lukowicz2002.pdf:PDF},
  keywords = {action, sensors},
  owner = {chris},
  posted-at = {2009-01-19 18:25:29},
  priority = {5},
  timestamp = {2009.12.03},
  url = {http://dx.doi.org/10.1007/3-540-45809-3_28}
}

@ARTICLE{Mathie2004,
  author = {Mathie, M. and Celler, B. and Lovell, N. and Coster, A.},
  title = {Classification of basic daily movements using a triaxial accelerometer},
  journal = {Medical and Biological Engineering and Computing},
  year = {2004},
  volume = {42},
  pages = {679--687},
  number = {5},
  month = {September},
  abstract = {Abstract\&nbsp;\&nbsp;A generic framework for the automated classification
	of human movements using an accelerometry monitoring system is introduced.
	The framework was structured around a binary decision tree in which
	movements were divided into classes and subclasses at different hierarchical
	levels. General distinctions between movements were applied in the
	top levels, and successively more detailed subclassifications were
	made in the lower levels of the tree. The structure was modular and
	flexible: parts of the tree could be reordered, pruned or extended,
	without the remainder of the tree being affected. This framework
	was used to develop a classifier to identify basic movements from
	the signals obtained from a single, waist-mounted triaxial accelerometer.
	The movements were first divided into activity and rest. The activities
	were classified as falls, walking, transition between postural orientations,
	or other movement. The postural orientations during rest were classified
	as sitting, standing or lying. In controlled laboratory studies in
	which 26 normal, healthy subjects carried out a set of basic movements,
	the sensitivity of every classification exceeded 87\%, and the specificity
	exceeded 94\%; the overall accuracy of the system, measured as the
	number of correct classifications across all levels of the hierarchy,
	was a sensitivity of 97.7\% and a specificity of 98.7\% over a data
	set of 1309 movements.},
  citeulike-article-id = {4636969},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/BF02347551},
  citeulike-linkout-1 = {http://www.springerlink.com/content/wm35501wq8352865},
  day = {12},
  doi = {10.1007/BF02347551},
  file = {Mathie2004.pdf:Mathie2004.pdf:PDF},
  owner = {chris},
  posted-at = {2009-05-26 20:42:51},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1007/BF02347551}
}

@INPROCEEDINGS{Maurer2006,
  author = {Maurer, U. and Rowe, A. and Smailagic, A. and Siewiorek, D.P.},
  title = {eWatch: a wearable sensor and notification platform},
  booktitle = {Proc. International Workshop on Wearable and Implantable Body Sensor
	Networks BSN 2006},
  year = {2006},
  pages = {4 pp.--145},
  doi = {10.1109/BSN.2006.24},
  file = {Maurer2006.pdf:Maurer2006.pdf:PDF},
  keywords = {Bluetooth, biomedical equipment, electric sensing devices, patient
	monitoring, watches, wearable computers, Bluetooth communication,
	eWatch platform, notification platform, online nearest neighbor classification,
	power aware hardware, software architecture, wearable computing platform,
	wearable sensors, wireless links, wrist watch form factor},
  owner = {chris},
  timestamp = {2009.12.03}
}

@MISC{Miluzzo2009,
  author = {Miluzzo, E., and Oakley, J., and Lu, H., and Lane, N., and Peterson,
	R., and Campbell, A.},
  title = {Evaluating the iPhone as a Mobile Platform for People-Centric Sensing
	Applications},
  year = {2009},
  comment = {No background apps on iPhone, no access to BT or Wifi stacks.},
  file = {Miluzzo2009.pdf:Miluzzo2009.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@INPROCEEDINGS{Nicolai2006,
  author = {Tom Nicolai and Nils Behrens and Holger Kenn},
  title = {Exploring Social Context with the Wireless Rope},
  booktitle = {In Proc. Workshop MONET: LNCS 4277},
  year = {2006},
  comment = {Definition of familiar strangers. DynStra/DynFam formula.},
  file = {Nicolai2006.pdf:Nicolai2006.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@INPROCEEDINGS{Nurmi2006,
  author = {Nurmi, Petteri and Koolwaaij, Johan},
  title = {Identifying meaningful locations},
  booktitle = {Mobile and Ubiquitous Systems: Networking \& Services, 2006 Third
	Annual International Conference on},
  year = {2006},
  pages = {1--8},
  abstract = {Existing context-aware mobile applications often rely on location
	information. However, raw location data such as GPS coordinates or
	GSM cell identifiers are usually meaningless to the user and, as
	a consequence, researchers have proposed different methods for inferring
	so-called places from raw data. The places are locations that carry
	some meaning to user and to which the user can potentially attach
	some (meaningful) semantics. Examples of places include home, work
	and airport. A lack in existing work is that the labeling has been
	done in an ad hoc fashion and no motivation has been given for why
	places would be interesting to the user. As our first contribution
	we use social identity theory to motivate why some locations really
	are significant to the user. We also discuss what potential uses
	for location information social identity theory implies. Another
	flaw in the existing work is that most of the proposed methods are
	not suited to realistic mobile settings as they rely on the availability
	of GPS information. As our second contribution we consider a more
	realistic setting where the information consists of GSM cell transitions
	that are enriched with GPS information whenever a GPS device is available.
	We present four different algorithms for this problem and compare
	them using real data gathered throughout Europe. In addition, we
	analyze the suitability of our algorithms for mobile devices},
  citeulike-article-id = {2420217},
  citeulike-linkout-0 = {http://dx.doi.org/10.1109/MOBIQ.2006.340429},
  citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4141782},
  comment = {Algorithms for identifying places, use cases, recognise commuting},
  doi = {10.1109/MOBIQ.2006.340429},
  file = {Nurmi2006.pdf:Nurmi2006.pdf:PDF},
  journal = {Mobile and Ubiquitous Systems: Networking \& Services, 2006 Third
	Annual International Conference on},
  keywords = {cs-location, read},
  owner = {chris},
  posted-at = {2008-02-24 02:03:54},
  priority = {2},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1109/MOBIQ.2006.340429}
}

@ARTICLE{Parkka2006,
  author = {Parkka, J. and Ermes, M. and Korpipaa, P. and Mantyjarvi, J. and
	Peltola, J. and Korhonen, I.},
  title = {Activity classification using realistic data from wearable sensors},
  journal = {Information Technology in Biomedicine, IEEE Transactions on},
  year = {2006},
  volume = {10},
  pages = {119--128},
  number = {1},
  abstract = {Automatic classification of everyday activities can be used for promotion
	of health-enhancing physical activities and a healthier lifestyle.
	In this paper, methods used for classification of everyday activities
	like walking, running, and cycling are described. The aim of the
	study was to find out how to recognize activities, which sensors
	are useful and what kind of signal processing and classification
	is required. A large and realistic data library of sensor data was
	collected. Sixteen test persons took part in the data collection,
	resulting in approximately 31 h of annotated, 35-channel data recorded
	in an everyday environment. The test persons carried a set of wearable
	sensors while performing several activities during the 2-h measurement
	session. Classification results of three classifiers are shown: custom
	decision tree, automatically generated decision tree, and artificial
	neural network. The classification accuracies using leave-one-subject-out
	cross validation range from 58 to 97\% for custom decision tree classifier,
	from 56 to 97\% for automatically generated decision tree, and from
	22 to 96\% for artificial neural network. Total classification accuracy
	is 82\% for custom decision tree classifier, 86\% for automatically
	generated decision tree, and 82\% for artificial neural network.},
  booktitle = {Information Technology in Biomedicine, IEEE Transactions on},
  citeulike-article-id = {3759728},
  citeulike-linkout-0 = {http://dx.doi.org/10.1109/TITB.2005.856863},
  citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1573714},
  citeulike-linkout-2 = {http://dx.doi.org/http://dx.doi.org/10.1109/TITB.2005.856863},
  citeulike-linkout-3 = {http://dx.doi.org/10.1109/TITB.2005.856863},
  doi = {10.1109/TITB.2005.856863},
  file = {Parkka2006.pdf:Parkka2006.pdf:PDF},
  keywords = {activity, coact, health, walton, wearable},
  owner = {chris},
  pdf = {Parkka2006.pdf},
  posted-at = {2008-12-09 14:55:55},
  priority = {2},
  timestamp = {2009.12.03},
  url = {http://dx.doi.org/10.1109/TITB.2005.856863}
}

@MISC{Patterson2004,
  author = {Donald J. Patterson and Dieter Fox and Henry Kautz and Kenneth Fishkin
	and Mike Perkowitz and Matthai Philipose},
  title = {Contextual Computer Support for Human Activity},
  year = {2004},
  citeseercitationcount = {0},
  citeseerurl = {http://citeseer.ist.psu.edu/635960.html},
  file = {Patterson2004.pdf:Patterson2004.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@MISC{Philipose2003,
  author = {Matthai Philipose and Sunny Consolvo and Kenneth Fishkin and Perkowitz
	Ian Smith},
  title = {Fast, Detailed Inference of Diverse Daily Human Activities},
  year = {2003},
  file = {Philipose2003.pdf:Philipose2003.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@ARTICLE{Philipose2004,
  author = {Philipose, M. and Fishkin, K.P. and Perkowitz, M. and Patterson,
	D.J. and Fox, D. and Kautz, H. and Hahnel, D.},
  title = {Inferring activities from interactions with objects},
  journal = IEEE_M_PVC,
  year = {2004},
  volume = {3},
  pages = {50--57},
  number = {4},
  doi = {10.1109/MPRV.2004.7},
  file = {Philipose2004.pdf:Philipose2004.pdf:PDF},
  issn = {1536-1268},
  keywords = {computerised monitoring, data mining, home automation, home computing,
	radiofrequency identification, ubiquitous computing, ADL inferencing,
	ADL monitoring, Proactive Activity Toolkit, daily living activity
	recognition, daily living activity recording, data mining, elder
	care, pervasive computing, probabilistic inference engine, radio-frequency-identification
	technology, ADL monitoring, Proact, Proactive Activity Toolkit, context-aware
	computing, sensor networks},
  owner = {chris},
  timestamp = {2009.12.01}
}

@MISC{Philipose2003a,
  author = {Philipose, M. and Fishkin, K. and Perkowitz, M. and Patterson, D.
	and H\"ahnel, D.},
  title = {The Probabilistic Activity Toolkit: Towards Enabling Activity-Aware
	Computer Interfaces},
  year = {2003},
  file = {Philipose2003a.pdf:Philipose2003a.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@ARTICLE{Raento2005,
  author = {Raento, Mika and Oulasvirta, Antti and Petit, Renaud and Toivonen,
	Hannu},
  title = {ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications},
  journal = {IEEE Pervasive Computing},
  year = {2005},
  volume = {4},
  pages = {51--59},
  number = {2},
  month = {April},
  abstract = {ContextPhone is an open-source prototyping platform for context-aware
	mobile applications. Its development was based on an iterative, human-centered
	strategy aimed at enabling real-world applications that are easily
	integrated into users\&\#253; everyday lives. The strategy included
	rapid response to feedback from field evaluations. The developers
	also studied other applications as well as general mobility issues.
	Their work resulted in prioritized design goals, including an emphasis
	on context, unobtrusiveness, truthfulness, seamfulness, timeliness
	and fast interaction. These design goals have been realized in several
	robust components running on top of the Series 60 Smartphone platform.
	These components include basic services like error recovery and service
	starting, sensors for gathering context data, communication channels
	for interacting with the outside world, and customizable versions
	of the Smartphone applications. Several real-world applications have
	been built on top of ContextPhone and the platform is released under
	an open-source license for use in further research.},
  address = {Piscataway, NJ, USA},
  booktitle = {Pervasive Computing, IEEE},
  citeulike-article-id = {2926228},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1070601.1070628},
  citeulike-linkout-1 = {http://dx.doi.org/10.1109/MPRV.2005.29},
  citeulike-linkout-2 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1427649},
  doi = {10.1109/MPRV.2005.29},
  file = {Raento2005.pdf:Raento2005.pdf:PDF},
  issn = {1536-1268},
  keywords = {adaptive\_interfaces, location\_aware\_computing},
  owner = {chris},
  posted-at = {2008-06-25 17:18:22},
  priority = {2},
  publisher = {IEEE Educational Activities Department},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1109/MPRV.2005.29}
}

@INPROCEEDINGS{Ravi2005,
  author = {Ravi, Nishkam and Nikhil, D. and Mysore, Preetham and Littman, Michael
	L.},
  title = {Activity recognition from accelerometer data},
  booktitle = {Proceedings of the Seventeenth Conference on Innovative Applications
	of Artificial Intelligence(IAAI},
  year = {2005},
  pages = {1541--1546},
  abstract = {Activity recognition fits within the bigger framework of context awareness.
	In this paper, we report on our efforts to recognize user activity
	from accelerometer data. Activity recognition is formulated as a
	classification problem. Performance of base-level classifiers and
	meta-level classifiers is compared. Plurality Voting is found to
	perform consistently well across different settings.},
  citeulike-article-id = {5157220},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.92.1333},
  citeulike-linkout-1 = {https://www.aaai.org/Papers/IAAI/2005/IAAI05-013.pdf},
  file = {Ravi2005.pdf:Ravi2005.pdf:PDF},
  keywords = {accelerometer, activity-inferencing},
  owner = {chris},
  posted-at = {2009-07-15 10:33:20},
  priority = {2},
  timestamp = {2009.12.06},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.92.1333}
}

@ARTICLE{Reynolds2008,
  author = {Reynolds, F.},
  title = {Camera Phones: A Snapshot of Research and Applications},
  journal = IEEE_M_PVC,
  year = {2008},
  volume = {7},
  pages = {16--19},
  number = {2},
  month = {April--June },
  comment = {"over one billion camera phones were sold last year"},
  doi = {10.1109/MPRV.2008.28},
  file = {Reynolds2008.pdf:Reynolds2008.pdf:PDF},
  owner = {chris},
  pdf = {Reynolds2008.pdf},
  timestamp = {2009.12.01}
}

@INPROCEEDINGS{Rudstroem2004,
  author = {Asa Rudstr\"om and Martinn Svensson and Martin Svensson and Rickard
	C\"oster and Kristina H\"o\"ok},
  title = {MobiTip: Using Bluetooth as a Mediator of Social Context},
  booktitle = {In Ubicomp 2004 Adjunct Proceedings},
  year = {2004},
  file = {Rudstroem2004.pdf:Rudstroem2004.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@MISC{Salber1999,
  author = {Salber, Daniel and Dey, Anind K. and Abowd, Gregory D.},
  title = {The Context Toolkit: Aiding the Development of Context-Enabled Applications},
  year = {1999},
  abstract = {Context-enabled applications are just emerging and promise richer
	interaction by taking environmental context into account. However,
	they are difficult to build due to their distributed nature and the
	use of unconventional sensors. The concepts of toolkits and widget
	libraries in graphical user interfaces has been tremendously successful,
	allowing programmers to leverage off existing building blocks to
	build interactive systems more easily. We introduce the concept of
	context widgets that mediate between the environment and the application
	in the same way graphical widgets mediate between the user and the
	application. We illustrate the concept of context widgets with the
	beginnings of a widget library we have developed for sensing presence,
	identity and activity of people and things. We assess the success
	of our approach with two example context-enabled applications we
	have built and an existing application to which we have added contextsensing
	capabilities.},
  citeulike-article-id = {3753280},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.2110},
  citeulike-linkout-1 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.2110},
  file = {Salber1999.pdf:Salber1999.pdf:PDF},
  keywords = {applications, context, context\_awareness, toolkit},
  owner = {chris},
  pages = {434--441},
  posted-at = {2008-12-07 12:33:46},
  priority = {4},
  timestamp = {2009.12.06},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.2110}
}

@MISC{Schapire1999,
  author = {Schapire, Robert E.},
  title = {A Brief Introduction to Boosting},
  year = {1999},
  abstract = {Boosting is a general method for improving the accuracy of any given
	learning algorithm. This short paper introduces the boosting algorithm
	AdaBoost, and explains the underlying theory of boosting, including
	an explanation of why boosting often does not suffer from overfitting.
	Some examples of recent applications of boosting are also described.
	Background Boosting is a general method which attempts to \&\#034;boost\&\#034;
	the accuracy of any given learning algorithm. Boosting has its roots
	in a theoretical framework for studying machine learning called the
	\&\#034;PAC\&\#034; learning model, due to Valiant [37]; see Kearns
	and Vazirani [24] for a good introduction to this model. Kearns and
	Valiant [22, 23] were the first to pose the question of whether a
	\&\#034;weak\&\#034; learning algorithm which performs just slightly
	better than random guessing in the PAC model can be \&\#034;boosted\&\#034;
	into an arbitrarily accurate \&\#034;strong\&\#034; learning algorithm.
	Schapire [30] came up with the first provable polynomial-time boosting
	algorithm in ...},
  citeulike-article-id = {6212085},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.8772},
  file = {Schapire1999.pdf:Schapire1999.pdf:PDF},
  keywords = {boosting},
  owner = {chris},
  posted-at = {2009-11-25 20:48:42},
  priority = {2},
  timestamp = {2009.12.03},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.8772}
}

@MISC{Schilit1994,
  author = {Bill Schilit and Norman Adams and Roy Want},
  title = {Context-Aware Computing Applications},
  year = {1994},
  citeseercitationcount = {0},
  citeseerurl = {http://citeseer.ist.psu.edu/339782.html},
  file = {Schilit1994.pdf:Schilit1994.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@MISC{Schmidt2008,
  author = {Albrecht Schmidt and Kofi Asante Aidoo and Antti Takaluoma and Urpo
	Tuomela and Kristof Van Laerhoven and Walter Van de Velde},
  title = {iLearn on the iPhone: Real-Time Human Activity Classification on
	Commodity Mobile Phones},
  year = {2008},
  file = {Schmidt2008.pdf:Schmidt2008.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.03}
}

@ARTICLE{Schmidt1999,
  author = {Schmidt, A. and Aidoo, K. A. and Takaluoma, A. and Tuomela, U. and
	Van Laerhoven, K. and Van de Velde, W.},
  title = {Advanced Interaction in Context},
  journal = {Lecture Notes in Computer Science},
  year = {1999},
  volume = {1707},
  pages = {89--??},
  abstract = {. Mobile information appliances are increasingly used in numerous
	
	different situations and locations, setting new requirements to their
	interaction
	
	methods. When the user's situation, place or activity changes, the
	functionality
	
	of the device should adapt to these changes. In this work we propose
	a layered
	
	real-time architecture for this kind of context-aware adaptation based
	on
	
	redundant collections of low-level sensors. Two kinds of sensors are
	
	distinguished: physical and logical...},
  citeulike-article-id = {1284635},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.2408},
  file = {Schmidt1999.pdf:Schmidt1999.pdf:PDF},
  keywords = {context, mobile, pervasive, ubicomp},
  owner = {chris},
  posted-at = {2007-05-09 07:15:21},
  priority = {4},
  timestamp = {2009.12.03},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.2408}
}

@MISC{Shen2004,
  author = {Jianqiang Shen},
  title = {Machine Learning for Activity Recognition},
  year = {2004},
  file = {Shen2004.pdf:Shen2004.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@INPROCEEDINGS{Siewiorek2003,
  author = {Siewiorek, D. and Smailagic, A. and Furukawa, J. and Krause, A. and
	Moraveji, N. and Reiger, K. and Shaffer, J. and Wong, Fei L.},
  title = {SenSay: a context-aware mobile phone},
  booktitle = {Wearable Computers, 2003. Proceedings. Seventh IEEE International
	Symposium on},
  year = {2003},
  pages = {248--249},
  citeulike-article-id = {898575},
  citeulike-linkout-0 = {http://dx.doi.org/10.1109/ISWC.2003.1241422},
  citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1241422},
  doi = {10.1109/ISWC.2003.1241422},
  file = {Siewiorek2003.pdf:Siewiorek2003.pdf:PDF},
  journal = {Wearable Computers, 2003. Proceedings. Seventh IEEE International
	Symposium on},
  keywords = {aware, context, mobile, phone},
  owner = {chris},
  posted-at = {2008-09-16 14:38:06},
  priority = {2},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1109/ISWC.2003.1241422}
}

@INPROCEEDINGS{Song2005,
  author = {Song, K. and Wang, Y.},
  title = {Remote Activity Monitoring of the Elderly Using a Two-Axis Accelerometer},
  booktitle = {CACS Automatic Control Conference},
  year = {2005},
  file = {Song2005.pdf:Song2005.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@ARTICLE{Stiefmeier2008,
  author = {Stiefmeier, T. and Roggen, D. and Troster, G. and Ogris, G. and Lukowicz,
	P.},
  title = {Wearable Activity Tracking in Car Manufacturing},
  journal = IEEE_M_PVC,
  year = {2008},
  volume = {7},
  pages = {42--50},
  number = {2},
  month = {April--June },
  doi = {10.1109/MPRV.2008.40},
  file = {Stiefmeier2008.pdf:Stiefmeier2008.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@ARTICLE{Tentori2008,
  author = {Tentori, M. and Favela, J.},
  title = {Activity-Aware Computing for Healthcare},
  journal = IEEE_M_PVC,
  year = {2008},
  volume = {7},
  pages = {51--57},
  number = {2},
  month = {April--June },
  doi = {10.1109/MPRV.2008.24},
  file = {Tentori2008.pdf:Tentori2008.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@INPROCEEDINGS{Terry2002,
  author = {Terry, Michael and Mynatt, Elizabeth D. and Ryall, Kathy and Leigh,
	Darren},
  title = {Social net: using patterns of physical proximity over time to infer
	shared interests},
  booktitle = {CHI '02: CHI '02 extended abstracts on Human factors in computing
	systems},
  year = {2002},
  pages = {816--817},
  address = {New York, NY, USA},
  publisher = {ACM Press},
  citeulike-article-id = {934249},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=506443.506612},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/506443.506612},
  doi = {10.1145/506443.506612},
  file = {Terry2002.pdf:Terry2002.pdf:PDF},
  isbn = {1581134541},
  keywords = {networking, social},
  owner = {chris},
  posted-at = {2008-08-21 15:56:49},
  priority = {2},
  timestamp = {2009.12.06},
  url = {http://dx.doi.org/10.1145/506443.506612}
}

@ARTICLE{Voida2002,
  author = {Voida, S. and Mynatt, E.D. and MacIntyre, B. and Corso, G.M.},
  title = {Integrating virtual and physical context to support knowledge workers},
  journal = IEEE_M_PVC,
  year = {2002},
  volume = {1},
  pages = {73--79},
  number = {3},
  doi = {10.1109/MPRV.2002.1037725},
  file = {Voida2002.pdf:Voida2002.pdf:PDF},
  issn = {1536-1268},
  keywords = {distributed processing, groupware, management information systems,
	user interfaces, Kimura system, data sources, electronic whiteboard,
	knowledge workers, networked peripheral devices, pervasive computing},
  owner = {chris},
  timestamp = {2009.12.01}
}

@INPROCEEDINGS{Wang2009,
  author = {Wang, Yi and Lin, Jialiu and Annavaram, Murali and Jacobson, Quinn
	A. and Hong, Jason and Krishnamachari, Bhaskar and Sadeh, Norman},
  title = {A framework of energy efficient mobile sensing for automatic user
	state recognition},
  booktitle = {MobiSys '09: Proceedings of the 7th international conference on Mobile
	systems, applications, and services},
  year = {2009},
  pages = {179--192},
  address = {New York, NY, USA},
  publisher = {ACM},
  doi = {http://doi.acm.org/10.1145/1555816.1555835},
  file = {Wang2009.pdf:Wang2009.pdf:PDF},
  isbn = {978-1-60558-566-6},
  location = {Krak\'{o}w, Poland},
  owner = {chris},
  pdf = {Wang2009.pdf},
  timestamp = {2009.12.03}
}

@INBOOK{Witten2000,
  chapter = {3},
  pages = {72-75},
  title = {Data Mining: Practical Machine Learning Tools and. Techniques with
	Java Implementations},
  publisher = {Morgan Kaufmann},
  year = {2000},
  author = {Ian H. Witten and Eibe Frank},
  owner = {chris},
  timestamp = {2010.01.14}
}

@INPROCEEDINGS{Wyatt2007,
  author = {Wyatt, D. and Choudhury, T. and Kautz, H.},
  title = {Capturing Spontaneous Conversation and Social Dynamics: A Privacy-Sensitive
	Data Collection Effort},
  booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal
	Processing ICASSP 2007},
  year = {2007},
  volume = {4},
  pages = {IV-213--IV-216},
  doi = {10.1109/ICASSP.2007.367201},
  file = {Wyatt2007.pdf:Wyatt2007.pdf:PDF},
  issn = {1520-6149},
  keywords = {data acquisition, speech intelligibility, UW dynamic social network,
	paralinguistic features, privacy constraints, privacy-sensitive data
	collection effort, prosodic features, social dynamics, spontaneous
	conversation, spontaneous face-to-face conversations, Data acquisition,
	oral communication, privacy, speech analysis},
  owner = {chris},
  timestamp = {2009.12.03}
}

@INPROCEEDINGS{Yang2008,
  author = {Sung-Ihk Yang and Sung-Bae Cho},
  title = {Recognizing human activities from accelerometer and physiological
	sensors},
  booktitle = {Proc. IEEE International Conference on Multisensor Fusion and Integration
	for Intelligent Systems MFI 2008},
  year = {2008},
  pages = {100--105},
  month = {20--22 Aug. },
  doi = {10.1109/MFI.2008.4648116},
  file = {Yang2008.pdf:Yang2008.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.03}
}

@MISC{ChangeWave2010,
  title = {http://www.changewaveresearch.com/articles/2010/01/smart\_phone\_20100104.html},
  owner = {chris},
  timestamp = {2010.01.12},
  url = {http://www.changewaveresearch.com/articles/2010/01/smart_phone_20100104.html}
}

@MISC{ComputerWorld2010,
  title = {http://www.computerworld.com/s/article/9139026/},
  owner = {chris},
  timestamp = {2010.01.12},
  url = {http://www.computerworld.com/s/article/9139026/Android_to_grab_No._2_spot_by_2012_says_Gartner}
}

@MISC{TwoFortyFourAm2010,
  title = {http://www.twofortyfouram.com/},
  owner = {chris},
  timestamp = {2010.01.14},
  url = {http://www.twofortyfouram.com/}
}

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viani2007\;Mathie2004\;Maurer2006\;Parkka2006\;Patterson2004\;Philipos
e2003\;Philipose2003a\;Philipose2004\;Quinlan1986\;Raento2005\;Ravi200
5\;Rudstroem2004\;Salber1999\;Schapire1999\;Schilit1994\;Schmidt1999\;
Siewiorek2003\;Song2005\;Stiefmeier2008\;Tentori2008\;Terry2002\;Voida
2002\;Wang2009\;Wyatt2007\;Yang2008\;;
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