An incorporation of deep temporal convolutional networks with hidden markov models post-processing for sensor-based human activity recognition-Reference-Cited by-同舟云学术

An incorporation of deep temporal convolutional networks with hidden markov models post-processing for sensor-based human activity recognition

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:The 11th International Symposium on Information and Communication Technology
language:
Short-container-title:

Author:

Trinh Linh¹^ORCID,Ha Bach²^ORCID

Affiliation:

1. Vingroup Advanced Analytics, Viet Nam

2. Hanoi University of Science and Technology, Viet Nam

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3568562.3568610

Reference32 articles.

1. 2019. Self-Attention Temporal Convolutional Network for Long-Term Daily Living Activity Detection . IEEE , 1–7. https://doi.org/10.1109/AVSS. 2019 .8909841 10.1109/AVSS.2019.8909841 2019. Self-Attention Temporal Convolutional Network for Long-Term Daily Living Activity Detection. IEEE, 1–7. https://doi.org/10.1109/AVSS.2019.8909841

2. Yoshua Bengio . 2013. Deep Learning of Representations: Looking Forward . In Statistical Language and Speech Processing . Springer Berlin Heidelberg , Berlin, Heidelberg , 1–37. Yoshua Bengio. 2013. Deep Learning of Representations: Looking Forward. In Statistical Language and Speech Processing. Springer Berlin Heidelberg, Berlin, Heidelberg, 1–37.

3. Yuqing Chen and Yang Xue. 2015. A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer. 1488-1492 pages. https://doi.org/10.1109/SMC.2015.263 10.1109/SMC.2015.263

4. Yuqing Chen and Yang Xue. 2015. A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer. 1488-1492 pages. https://doi.org/10.1109/SMC.2015.263

5. Binarized-BLSTM-RNN based Human Activity Recognition