Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations-Reference-Cited by-同舟云学术

Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations

Published:2016-09-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2016 ACM International Symposium on Wearable Computers
language:
Short-container-title:

Author:

Morales Francisco Javier Ordóñez¹,Roggen Daniel¹

Affiliation:

1. University of Sussex, Brighton, UK

Funder

Google Faculty Research Award

EPSRC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2971763.2971764

Reference23 articles.

1. 2008. Skoda dataset. http://www.ife.ee.ethz.ch/research/groups/Dataset. (2008). {Online; accessed 20-April-2016}. 2008. Skoda dataset. http://www.ife.ee.ethz.ch/research/groups/Dataset. (2008). {Online; accessed 20-April-2016}.

2. 2012. Opportunity dataset. https://archive.ics.uci.edu/ml/datasets/OPPORTUNITY+Activity+Recognition. (June 2012). {Online; accessed 20-April-2016}. 2012. Opportunity dataset. https://archive.ics.uci.edu/ml/datasets/OPPORTUNITY+Activity+Recognition. (June 2012). {Online; accessed 20-April-2016}.

3. Using unlabeled data in a sparse-coding framework for human activity recognition

4. D. Cook K. Feuz and N. C. Krishnan. 2013. Transfer learning for activity recognition: A survey. Knowledge and information systems 36 3 (2013) 537--556. D. Cook K. Feuz and N. C. Krishnan. 2013. Transfer learning for activity recognition: A survey. Knowledge and information systems 36 3 (2013) 537--556.

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