contextapi/papers/project.bib

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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},
  file = {Abowd1997.pdf:Abowd1997.pdf:PDF},
  owner = {chris},
  pdf = {Abowd1997.pdf},
  timestamp = {2009.12.01}
}

@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{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 },
  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},
  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 = {Carós, JS. and Chételat, 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},
  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}
}

@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 },
  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},
  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},
  file = {Eagle2004.pdf:Eagle2004.pdf:PDF},
  journal = {IEEE Pervasive, Special Issue on Smart Phones},
  owner = {chris},
  timestamp = {2009.12.01}
}

@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},
  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},
  file = {Garakani2009.pdf:Garakani2009.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@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.},
  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{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},
  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}
}

@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},
  comment = {NOTE: These notes were supplemented by looking at Liao's dissertation
	writeup of the same work.
	
	Overview: This is a supervised learning scheme done in several stages...
	the results from early stages are used to build the structure of
	the model in subsequent stages. Training is done for 3 people, and
	tested on a 4th person. For the test person, only GPS pings are used
	as input.
	
	specifics: * GPS traces from 4 people, 6 days per person * \~{}40,000
	GPS measurements per person * manually labeled all activities and
	significant places in these traces * used maximum pseudo-likelihood
	for learning
	
	hierarchical graphical model with 3 levels. top level: significant
	places (e.g. home, work, bus stop, parking lot, friend) midlevel:
	activity sequence (eg. walk, drive, visit, sleep, get on bus, pickup),
	bottom level: GPS trace (association to streeet map)
	
	* their typical GPS trace consists of approximately one GPS reading
	per second * GPS readings are segmented spatially; we have one activity
	node for each spatial segment of GPS pings ** e.g. a 12 hour stay
	at a single location is represented by a single activity node * if
	street map is available, segmentation is done in association with
	the street map (with 10m discretization) * "model can reason explicitly
	about the duration of a stay, for which dynamic models such as standard
	DBNs or HMMs have only limited support" [12,32]
	
	* two main groups of activities: navigation activities, and significant
	activities (in a single place, or at a transportation mode switch)
	* "to determine activities, our model relies heavily on temporal
	features, such as duration or time of day, and geographic information,
	such as locations of restaurants, stores, and bus stops." * "significant
	places are those locations that play a significnat role in the activities
	of a person: hoe, work, bus stops, parking lots typically used, homes
	of friends, etc...) model allows different activites to occur at
	same significant place; also, a significnat place can comprise mutiple
	different locations, mostly because of signal loss and GPS readings
	
	The CRF Model for Activity Recognition: 1) CRF for GPS denoising *
	break up street segments into 10 meter patches * measurement clique
	between each ping and all nearby street patches (Gaussian noise model
	from center of patch) * consistency cliques: put a Gaussian noise
	model on the \_difference\_ between the GPS displacement and the
	paired street patch displacement for consecutive pings * smoothness
	cliques: encourage street patch predictions to be stay on the same
	street, going in the same direction their conditional model for street
	patches given GPS pings is given in Eqn 26, bottom of page 10
	
	Output of the CRF model is taken as the spatial segmentation of the
	GPS pings... Hierarchical CRF is based on the segmentation, since
	one 'activity sequence' is associated with each sequence of points
	on the same 10m street patch * bottom layer of 3 layer CRF, now it's
	"local evidence" including: ** temporal information (e.g. time od
	day, day of week, duration of stay -- these can be discretized) clique
	functions are all binary indicators, one for every possible combination
	of temporal feature and activity ** average speed through segment
	-- discretized ** information extracted from geographic databses,
	such as whether a patch is on a bus route, close to abus stop, near
	a restaurant or grovery store; use indicator functions to model this
	information ** each activity node connected to its neighbors; e.g.
	extremely unlikely tha ta person will get on bus at one location
	and drive a car at neighboing location right afterwards (?)
	
	* Preliminary CRF\_0: just has activity nodes and local evidence nodes
	[notes from thesis] ** adjacent activity nodes are connected, and
	each activity node is connected to each 'ftr' with a pairwise potential
	** features are *** gps based: streetpatch, timeofday (first timestamp,
	discretized into Moring, Noon, AFternoon, Evening, Night), dayofweek,
	duration, *** geo database: nearrestaurant, nearstore, nearbusstop,
	onbusroute [latter extracted from geographic databaddses] *** average
	speed thru segment (discretized - 'to allow multimodality')
	
	* Significant Places [from thesis] ** from the MAP activity sequence,
	they then (by hand? they refer to an IsSignificant() function) associate
	each activity with whether or not it belongs to a significant place
	(e.g. transport does not connect to a significant place, while getting
	on / off a bus does ...); * spatial clustering performed on the locations
	of significant places * the cluster centers are the 'signiciant plces'
	* edge between each place label and each activity label in its vicinity
	* NOTE: the 'place nodes' are not dynamic; we find K lat,longs that
	are the locations of the significant places; what we still ned to
	infer are the labels for these significant places; e.g. several of
	the lat,longs can be 'shopping', or 'friend's house, etc... certain
	place labels are associated with certain types of activities * they
	want to also add features that count the number of 'home', 'workplace',
	etc., labels that are used. However, this requires making a clique
	containing all the places, which can be quite large...
	
	generate\_places algorithm: * really not clear -- clustering places
	associated with the same activity? how can you get any confidence
	in the activities?
	
	* Each place (node at the top level of 3 level CRF) connects to all
	activities that seem to occur in the same place -- built into the
	structure of the model * multiple activities may occur at the same
	place
	
	* each GPS associated with a 10m patch on a street edge * [20] inference
	using loopy belief propagation, and parameter learning using pseudo-likelihoodreferences
	to chase down: bennewitz [4] learn different motion paths between
	places, [23] how to figure out types of places;},
  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{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}
}

@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},
  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},
  file = {Nicolai2006.pdf:Nicolai2006.pdf:PDF},
  owner = {chris},
  timestamp = {2009.12.01}
}

@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},
  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 Hähnel, 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{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 = {Åsa Rudström and Martinn Svensson and Martin Svensson and Rickard
	Cöster and Kristina Höök},
  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{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},
  comment = {- history, algorithm, description of two bounds},
  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{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}
}

@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}
}

@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},
  owner = {chris},
  timestamp = {2009.12.03}
}

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