Human activity recognition with AutoML using smartphone radio data-Reference-Cited by-同舟云学术

Human activity recognition with AutoML using smartphone radio data

Published:2021-09-21 Issue: Volume: Page:
ISSN:
Container-title:Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers
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Author:

Balabka Dmitrijs¹,Shkliarenko Denys²

Affiliation:

1. Transport and Telecommunication Institute, Latvia and Transport and Telecommunication Institute, Latvia

2. None, Ukraine

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3460418.3479377

Reference34 articles.

1. Dmitrijs Balabka. 2019. Semi-supervised learning for human activity recognition using adversarial autoencoders. In UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers 685–688. DOI:https://doi.org/10.1145/3341162.3344854 Dmitrijs Balabka. 2019. Semi-supervised learning for human activity recognition using adversarial autoencoders. In UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers 685–688. DOI:https://doi.org/10.1145/3341162.3344854

2. Dmitrijs Balabka. 2021. SHL challenge 2021 solution source. Retrieved from https://github.com/dbalabka/shl-activity-recognition-2021/ Dmitrijs Balabka. 2021. SHL challenge 2021 solution source. Retrieved from https://github.com/dbalabka/shl-activity-recognition-2021/

3. The GeoJSON Format

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