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On using existing time-use study data for ubiquitous computing applications
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UbiComp; Vol. 344 archive
Proceedings of the 10th international conference on Ubiquitous computing table of contents
Seoul, Korea
SESSION: Ubicomp methods and tools table of contents
Pages 144-153  
Year of Publication: 2008
ISBN:978-1-60558-136-1
Authors
Kurt Partridge  Palo Alto Research Center, Palo Alto, CA
Philippe Golle  Palo Alto Research Center, Palo Alto, CA
Publisher
ACM  New York, NY, USA
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ABSTRACT

Governments and commercial institutions have conducted detailed time-use studies for several decades. In these studies, participants give a detailed record of their activities, locations, and other data over a day, week, or longer period. These studies are particularly valuable for the ubicomp community because of the large number of participants (often the tens of thousands), and because of their public availability. In this paper, we show how to use the data from these studies to provide validated and cheap (although noisy) classifiers, baseline metrics, and other benefits for activity inference applications.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Bao and Intille, "Activity Recognition from User-Annotated Acceleration Data," Pervasive Computing, 2004.
2
 
3
M. Csikszentmihalyi, Flow: The Psychology of Optimal Experience, Harper & Row USA, 1990.
 
4
K. Fisher, J. Tucker, and A. Jahandar, Technical Details of Time Use Studies, Institute for Social and Economic Research, University of Essex,; http://www.timeuse.org/mtus/.
 
5
 
6
S. Intille, E. M. Tapia, J. Rondoni, J. Beaudin, C. Kukla, S. Agarwal, et al., "Tools for Studying Behavior and Technology in Natural Settings," Ubiquitous Computing, 2003.
 
7
S. Katz, A. B. Ford, R. W. Moskowitz, B. A. Jackson, M. W. Jaffe, and K. L. White, "Studies of illness in the aged--The index of ADL: A standardized measure of biological and psychosocial functions," JAMA 185:914--919 (1963).
 
8
N. E. Klepeis, W. C. Nelson, W. R. Ott, J. P. Robinson, A. M. Tsang, P. Switzer, et al., "The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants," Journal of Exposure Analysis and Environmental Epidemiology, vol. 11, 2001.
 
9
J. Krumm and E. Horvitz, "Predestination: Inferring Destinations from Partial Trajectories," UbiComp, 2006, pp. 243--260.
 
10
J. Lester, T. Choudhury, N. Kern, G. Borriello, and B. Hannaford, "A Hybrid Discriminative/Generative Approach for Modeling Human Activities," Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2005), 2005.
 
11
L. Liao, D. Fox, and H. Kautz, "Location-based activity recognition using relational Markov networks," Proc. of the International Joint Conference on Artificial Intelligence (IJCAI), 2005.
 
12
B. Logan, J. Healey, M. Philipose, E. M. Tapia, and S. Intille, "A Long-Term Evaluation of Sensing Modalities for Activity Recognition." Proc. of Ubicomp 2007.
 
13
Lukowicz, Ward, Junker, Stäger, Tröster, Atrash, et al., "Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers," Pervasive Computing, 2004.
 
14
M. G. McNally, "The activity-based approach," Handbook of Transport Modelling, 2000, pp. 53--69.
 
15
W. M. Michelson, Time Use: Expanding Explanation in the Social Sciences, Paradigm Publishers, 2005.
 
16
B. Morgan, "Learning Commonsense Human-language Descriptions from Temporal and Spatial Sensornetwork Data," Massachusetts Institute of Technology, 2006.
 
17
S. N. Patel, J. A. Kientz, G. R. Hayes, S. Bhat, and G. D. Abowd, "Farther than you may think: An empirical investigation of the proximity of users to their mobile phones," Proceedings of Ubicomp, 2006.
 
18
W. E. Pentland, Time Use Research in the Social Sciences, Kluwer Academic Publishers, 1999.
 
19
W. Pentney, A. Popescu, S. Wang, and H. Kautz, "Sensor-Based Understanding of Daily Life via Large-Scale Use of Common Sense," Proceedings of AAAI, 2006.
 
20
 
21
J. P. Robinson, "The Time-Diary Method: Structure and Uses," Time Use Research in the Social Sciences, 1999.
 
22
K. J. Shelley, "Developing the American Time Use Survey Activity Classification System," Monthly Labor Review, vol. 128, 2005, pp. 3--15.
 
23
A. Shon, "Methodological and Operational Dimensions on Time-Use Survey in the Republic of Korea," International Seminar on Time Use Studies, 1999.
 
24
P. Singh and W. Williams, "LifeNet: a propositional model of ordinary human activity," Proceedings of the Workshop on Distributed and Collaborative Knowledge Capture (DC-KCAP), 2003.
 
25
A. Szalai, The use of time: daily activities of urban and suburban populations in twelve countries, Mouton, 1972.
 
26
J. Wolf, R. Guensler, and W. Bachman, "Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data," Transportation Research Record, vol. 1768, 2001, pp. 125--134.

Collaborative Colleagues:
Kurt Partridge: colleagues
Philippe Golle: colleagues