Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes-Reference-Cited by-同舟云学术

Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes

Published:2021 Issue: Volume: Page:111-125
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Bouchabou Damien,Nguyen Sao Mai^ORCID,Lohr Christophe,LeDuc Benoit,Kanellos Ioannis

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-0575-8_9

Reference20 articles.

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2. Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Transfer learning for time series classification. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1367–1376. IEEE (2018)

3. Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Discov. 33(4), 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1

4. Gochoo, M., Tan, T.H., Liu, S.H., Jean, F.R., Alnajjar, F.S., Huang, S.C.: Unobtrusive activity recognition of elderly people living alone using anonymous binary sensors and DCNN. IEEE J. Biomed. Health Inform. 23(2), 693–702 (2018)

5. Hamad, R.A., Hidalgo, A.S., Bouguelia, M.R., Estevez, M.E., Quero, J.M.: Efficient activity recognition in smart homes using delayed fuzzy temporal windows on binary sensors. IEEE J. Biomed. Health Inform. 24(2), 387–395 (2019)

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