Discovering latent structures for activity recognition in smart environments

Wen, Jiahui and Indulska, Jadwiga (2014). Discovering latent structures for activity recognition in smart environments. In: Bernady O. Apduhan, Yu Zheng, Yukikazu Nakamoto, Parimala Thulasiraman, Huansheng Ning and Yuqing Sun, Ubiquitous Intelligence and Computing, 2014 IEEE 11th Intl Conf on and IEEE 11th Intl Conf on and Autonomic and Trusted Computing, and IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UTC-ATC-ScalCom). 2014 IEEE UIC/ATC/ScalCom Multi-Conference, Bali, Indonesia, (140-147). 9-12 December 2014. doi:10.1109/UIC-ATC-ScalCom.2014.129


Author Wen, Jiahui
Indulska, Jadwiga
Title of paper Discovering latent structures for activity recognition in smart environments
Conference name 2014 IEEE UIC/ATC/ScalCom Multi-Conference
Conference location Bali, Indonesia
Conference dates 9-12 December 2014
Proceedings title Ubiquitous Intelligence and Computing, 2014 IEEE 11th Intl Conf on and IEEE 11th Intl Conf on and Autonomic and Trusted Computing, and IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UTC-ATC-ScalCom)
Journal name Proceedings - 2014 IEEE International Conference on Ubiquitous Intelligence and Computing, 2014 IEEE International Conference on Autonomic and Trusted Computing, 2014 IEEE International Conference on Scalable Computing and Communications and Associated Symposia/Workshops, UIC-ATC-ScalCom 2014
Place of Publication Piscataway, NJ, United States
Publisher IEEE (Institute of Electrical and Electronics Engineers)
Publication Year 2014
Sub-type Fully published paper
DOI 10.1109/UIC-ATC-ScalCom.2014.129
Open Access Status Not Open Access
ISBN 9781479976454
Editor Bernady O. Apduhan
Yu Zheng
Yukikazu Nakamoto
Parimala Thulasiraman
Huansheng Ning
Yuqing Sun
Start page 140
End page 147
Total pages 8
Language eng
Formatted Abstract/Summary
Activity recognition is of great importance for a variety of context-aware applications and especially for assistance provided for independent living of the elderly. One of the factors that has impact on the activity recognition accuracy is feature representation. Many approaches have been proposed to select the most discriminative subset of features for activity recognition, but they are data-dependent and related to real numbers gathered from wearable sensors/devices. However, feature representation and transformation in smart environments, in which the data is very often a discrete sensor reading, has not been fully explored. In this paper, we map the activity model in a smart environment to a topic model and leverage the hierarchical Dirichlet process (HDP) to discover hidden structures from the sensor reading sequences. We use a hybrid approach: we combine the discovered latent structures with discriminative classifier such as support vector machines (SVMs), and demonstrate, through comparison studies, its effectiveness in improving activity recognition accuracy and the robustness to sensor noises. The comparison studies are carried out on three publicly available datasets from smart environments.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Thu, 12 Mar 2015, 16:10:00 EST by Ms Dulcie Stewart on behalf of School of Information Technol and Elec Engineering