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- % This file was created with JabRef 2.3.1.
- % Encoding: UTF-8
-
- @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;Abowd1997.pdf:Abowd1997.pdf:PDF},
- owner = {chris},
- 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;Bannach2008.pdf:Bannach2008.pdf:PDF},
- owner = {chris},
- 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;Davies2008.pdf:Davies2008.pdf:PDF},
- issn = {1536-1268},
- keywords = {activity recognition, activity-based computing, context-aware computing},
- owner = {chris},
- 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\ \ We introduce a system for sensing complex social
- systems with data collected from 100 mobile phones over the course
- of 9\ 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 modications of SMO. These modied algorithms perform signicantly
- 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\ \ 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;Parkka2006.pdf:Parkka2006.pdf:PDF},
- keywords = {activity, coact, health, walton, wearable},
- owner = {chris},
- 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;Reynolds2008.pdf:Reynolds2008.pdf:PDF},
- owner = {chris},
- 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;Wang2009.pdf:Wang2009.pdf:PDF},
- isbn = {978-1-60558-566-6},
- location = {Krak\'{o}w, Poland},
- owner = {chris},
- 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/}
- }
-
- @comment{jabref-meta: selector_publisher:}
-
- @comment{jabref-meta: selector_author:}
-
- @comment{jabref-meta: selector_journal:}
-
- @comment{jabref-meta: selector_keywords:}
-
- @comment{jabref-meta: groupsversion:3;}
-
- @comment{jabref-meta: groupstree:
- 0 AllEntriesGroup:;
- 1 ExplicitGroup:Unread\;0\;Aha1991\;Bardram2005\;Bellotti2008\;Consolv
- o2008\;Eagle2006\;Horvitz1998\;Hudson2003\;Keerthi1999\;Lee2007\;Leste
- r2005\;Lester2006\;Liao2007\;Liao2007a\;Liao2007b\;Lukowicz2002\;Mahda
- viani2007\;Mathie2004\;Maurer2006\;Parkka2006\;Patterson2004\;Philipos
- e2003\;Philipose2003a\;Philipose2004\;Raento2005\;Ravi2005\;Rudstroem2
- 004\;Salber1999\;Schapire1999\;Schilit1994\;Schmidt1999\;Siewiorek2003
- \;Song2005\;Stiefmeier2008\;Tentori2008\;Terry2002\;Voida2002\;Wang200
- 9\;Wyatt2007\;Yang2008\;;
- }
|