Human Activity Detection Patterns: A Pilot Study for Unobtrusive Discovery of Daily Working Routine-Reference-Cited by-同舟云学术

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Human Activity Detection Patterns: A Pilot Study for Unobtrusive Discovery of Daily Working Routine

Published:2017-12-31 Issue: Volume: Page:143-148
ISSN:2194-5357
Container-title:Intelligent Human Systems Integration
language:
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Author:

Rifai Hicham,Kelly Paula,Shoji Yoshiki,Berry Damon,Zallio Matteo

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-73888-8_24

Reference15 articles.

1. Andrews, S., Ellis, D., Shaw, H., Piwek, L.: Beyond self-report: tools to compare estimated and real-world smartphone use. PLoS One 10, e0139004 (2015)

2. Harari, G., Lane, N., Wang, R., Crosier, B., Campbell, A., Gosling, S.: Using smartphones to collect behavioral data in psychological science. Perspect. Psychol. Sci. 11, 838–854 (2016)

3. Gunay, H., O’Brien, W., Beausoleil-Morrison, I.: A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices. Build. Environ. 70, 31–47 (2013)

4. Liu, L., Peng, Y., Wang, S., Liu, M., Huang, Z.: Complex activity recognition using time series pattern dictionary learned from ubiquitous sensors. Inf. Sci. 340–341, 41–57 (2016)

5. Sun, K., Yan, D., Hong, T., Guo, S.: Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration. Build. Environ. 79, 1–12 (2014)

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1. Events that Affect Urban Mobility Patterns: an Analysis of Beijing GPS Data;2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD);2019-05

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