Open Access
Issue
MATEC Web Conf.
Volume 189, 2018
2018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
Article Number 10001
Number of page(s) 9
Section Bio & Human Engineering
DOI https://doi.org/10.1051/matecconf/201818910001
Published online 10 August 2018
  1. Atallah L and Louis B et al. Sensor Positioning for Activity Recognition Using Wearable Accelerometers IEEE Transactions on Biomedical Circuits & Systems 5.4(2011):320-329. [Google Scholar]
  2. Jin S Y and H J Essential Body-Joint and Atomic Action Detection for Human Activity Recognition Using Longest Common Subsequence Algorithm. International Conference on Computer VisionSpringer-Verlag 2012:148-159. [Google Scholar]
  3. Rieping K Englebienne G and Kröse B. Behavior analysis of elderly using topic models Pervasive & Mobile Computing 15 C(2014):181-199. [CrossRef] [Google Scholar]
  4. Sadek S Alhamadi A and Michaelis B et al. A Fast Statistical Approach for Human Activity Recognition International Journal of Intelligence Science 2.1(2012):9-15. [Google Scholar]
  5. Chen L Hoey J Nugent C D, et al. Sensor-Based Activity Recognition[J] IEEE Transactions on Systems Man & Cybernetics Part C 2012 42(6):790-808. [CrossRef] [Google Scholar]
  6. Tapia E M, Intille S S, Larson K Activity Recognition in the Home Using Simple and Ubiquitous Sensors Pervasive Computing Springer Berlin Heidelberg 2003:158-175. [Google Scholar]
  7. Zhao Z Chen Y and Liu J et al. Cross-people mobile-phone based activity recognition International Joint Conference on Artificial Intelligence AAAI Press 2011:2545-2550. [Google Scholar]
  8. Chikhaoui B Wang S Pigot H. ADR-SPLDA: Activity discovery and recognition by combining sequential patterns and latent Dirichlet allocation[J] Pervasive & Mobile Computing 2012 8(6):845-862. [Google Scholar]
  9. Liu L Peng Y and Liu M et al. Sensor-based human activity recognition system with a multilayered model using time series shapelets[J] Knowledge-Based Systems 2015, 90(C):138-152. [Google Scholar]
  10. Wen J Indulska J Wang Z. Discovering Latent Structures for Activity Recognition in Smart Environments[C] Ubiquitous Intelligence and Computing 2014 IEEE Intl Conf on and IEEE, Intl Conf on and Autonomic and Trusted Computing and IEEE, Intl Conf on Scalable Computing and Communications and ITS Associated Workshops. IEEE 2014:140-147. [Google Scholar]
  11. Wen J Zhong M Wang Z. Activity recognition with weighted frequent patterns mining in smart environments[J]. Expert Systems with Applications 2015 42(17– 18):6423-6432. [CrossRef] [Google Scholar]
  12. Johnson N Hogg D. Learning the distribution of object trajectories for event recognition[C] British Conference on Machine Vision BMVA Press 1995:583-592. [Google Scholar]
  13. Ahad M A R, Tan J K, Kim H S, et al. Analysis of Motion Self-Occlusion Problem Due to Motion Overwriting for Human Activity Recognition[J] Journal of Multimedia 2010 5(1). [Google Scholar]
  14. [14] Ahad A R, Ogata T Tan J K, et al. Performance of Multi-directional MHI for Human Motion Recognition in the Presence of Outliers[J] Conference of the IEEE 2007:2366-2370. [Google Scholar]
  15. Könönen V Mäntyjärvi J Similä H, et al. Automatic feature selection for context recognition in mobile devices[J] Pervasive & Mobile Computing 2010 6(2):181-197. [Google Scholar]
  16. Zhu C Sheng W. Motion-and location-based online human daily activity recognition[J] Pervasive & Mobile Computing 2011 7(2):256-269. [Google Scholar]
  17. Cook D Crandall A Thomas B et al. CASAS: A Smart Home in a Box[J] Computer 2013 46(7):62-69. [Google Scholar]
  18. Cook D. [n. d.]. CASAS smart home project. http:www.tp-ontrol.hu/index.php/TPToolbox ([n. d.]). [Google Scholar]
  19. Kasteren T V, Noulas A Englebienne G. Accurate activity recognition in a home setting[C] International Conference on Ubiquitous Computing ACM 2008:1-9. [Google Scholar]
  20. Cook D. [n. d.]. WSU Datasets. http:ailab.wsu.edu/casas/datasets/index.html. ([n. d.]). [Google Scholar]

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