Standard
The making of a dataset for smart spaces. / Kim, E.
; Helal, Sumi; Lee, J. et al.
International Conference on Ubiquitous Intelligence and Computing, UIC 2010. ed. / Z. Yu; R. Liscano; G. Chen; D. Zhang; X. Zhou. Berlin: Springer, 2010. p. 110-124 (Lecture Notes in Computer Science; Vol. 6406).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Kim, E
, Helal, S, Lee, J & Hossain, S 2010,
The making of a dataset for smart spaces. in Z Yu, R Liscano, G Chen, D Zhang & X Zhou (eds),
International Conference on Ubiquitous Intelligence and Computing, UIC 2010. Lecture Notes in Computer Science, vol. 6406, Springer, Berlin, pp. 110-124.
https://doi.org/10.1007/978-3-642-16355-5-11APA
Kim, E.
, Helal, S., Lee, J., & Hossain, S. (2010).
The making of a dataset for smart spaces. In Z. Yu, R. Liscano, G. Chen, D. Zhang, & X. Zhou (Eds.),
International Conference on Ubiquitous Intelligence and Computing, UIC 2010 (pp. 110-124). (Lecture Notes in Computer Science; Vol. 6406). Springer.
https://doi.org/10.1007/978-3-642-16355-5-11Vancouver
Kim E
, Helal S, Lee J, Hossain S.
The making of a dataset for smart spaces. In Yu Z, Liscano R, Chen G, Zhang D, Zhou X, editors, International Conference on Ubiquitous Intelligence and Computing, UIC 2010. Berlin: Springer. 2010. p. 110-124. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-16355-5-11
Author
Kim, E.
; Helal, Sumi ; Lee, J. et al. /
The making of a dataset for smart spaces. International Conference on Ubiquitous Intelligence and Computing, UIC 2010. editor / Z. Yu ; R. Liscano ; G. Chen ; D. Zhang ; X. Zhou. Berlin : Springer, 2010. pp. 110-124 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{2a699d2bcabf48a7862a17bd343a3d77,
title = "The making of a dataset for smart spaces",
abstract = "In this paper we propose a two-phase methodology for designing datasets that can be used to test and evaluate activity recognition algorithms. The trade offs between time, cost and recognition performance is one challenge. The effectiveness of a dataset, which contrasts the incremental performance gain with the increase in time, efforts, and number and cost of sensors is another challenging area that is often overlooked. Our proposed methodology is iterative and adaptive and addresses issues of sensor use modality and its effect on overall performance. We present our methodology and provide an assessment for its effectiveness using both a simulation model and a real world deployment. {\textcopyright} 2010 Springer-Verlag.",
keywords = "Activity Dataset, Activity Dataset Design, Activity Recognition, Pervasive Space Simulation, Activity recognition, Data sets, Performance Gain, Real world deployment, Recognition performance, Simulation model, Smart space, Trade off, Computer simulation, Sensors, Ubiquitous computing, Data processing",
author = "E. Kim and Sumi Helal and J. Lee and S. Hossain",
year = "2010",
doi = "10.1007/978-3-642-16355-5-11",
language = "English",
isbn = "3642163548",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "110--124",
editor = "Z. Yu and R. Liscano and Chen, {G. } and D. Zhang and X. Zhou",
booktitle = "International Conference on Ubiquitous Intelligence and Computing, UIC 2010",
}
RIS
TY - GEN
T1 - The making of a dataset for smart spaces
AU - Kim, E.
AU - Helal, Sumi
AU - Lee, J.
AU - Hossain, S.
PY - 2010
Y1 - 2010
N2 - In this paper we propose a two-phase methodology for designing datasets that can be used to test and evaluate activity recognition algorithms. The trade offs between time, cost and recognition performance is one challenge. The effectiveness of a dataset, which contrasts the incremental performance gain with the increase in time, efforts, and number and cost of sensors is another challenging area that is often overlooked. Our proposed methodology is iterative and adaptive and addresses issues of sensor use modality and its effect on overall performance. We present our methodology and provide an assessment for its effectiveness using both a simulation model and a real world deployment. © 2010 Springer-Verlag.
AB - In this paper we propose a two-phase methodology for designing datasets that can be used to test and evaluate activity recognition algorithms. The trade offs between time, cost and recognition performance is one challenge. The effectiveness of a dataset, which contrasts the incremental performance gain with the increase in time, efforts, and number and cost of sensors is another challenging area that is often overlooked. Our proposed methodology is iterative and adaptive and addresses issues of sensor use modality and its effect on overall performance. We present our methodology and provide an assessment for its effectiveness using both a simulation model and a real world deployment. © 2010 Springer-Verlag.
KW - Activity Dataset
KW - Activity Dataset Design
KW - Activity Recognition
KW - Pervasive Space Simulation
KW - Activity recognition
KW - Data sets
KW - Performance Gain
KW - Real world deployment
KW - Recognition performance
KW - Simulation model
KW - Smart space
KW - Trade off
KW - Computer simulation
KW - Sensors
KW - Ubiquitous computing
KW - Data processing
U2 - 10.1007/978-3-642-16355-5-11
DO - 10.1007/978-3-642-16355-5-11
M3 - Conference contribution/Paper
SN - 3642163548
SN - 9783642163548
T3 - Lecture Notes in Computer Science
SP - 110
EP - 124
BT - International Conference on Ubiquitous Intelligence and Computing, UIC 2010
A2 - Yu, Z.
A2 - Liscano, R.
A2 - Chen, G.
A2 - Zhang, D.
A2 - Zhou, X.
PB - Springer
CY - Berlin
ER -
