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Dense CNN and IndRNN for the Sussex-Huawei Locomotion-Transportation Recognition Challenge

Conference Paper


Abstract

  • The Sussex-Huawei Locomotion Challenge (SHL) 2021 organized at the HASCA Workshop of UbiComp 2021 is to recognize human activity by using GPS reception, GPS location, Wifi reception and GSM cell tower scans data. Compared with the previous challenge, this challenge is more difficult. In this paper, our team (NUC) summarize our submission to the competition. We propose a framework of deep learning including one-dimensional (1D) Dense CNN and Dense IndRNN networks to explore short and long-Term spatial temporal information. The location and Wifi features are extracted and normalized to feed into deep learning networks. Finally, the decision fusion is utilized to improve performance.

Publication Date

  • 2021

Publisher

Citation

  • Li, C., Li, S., Gao, Y., Guo, J., Chen, P., & Li, W. (2021). Dense CNN and IndRNN for the Sussex-Huawei Locomotion-Transportation Recognition Challenge. In UbiComp/ISWC 2021 - 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 (pp. 380-384). doi:10.1145/3460418.3479378

Scopus Eid

  • 2-s2.0-85115944374

Web Of Science Accession Number

Start Page

  • 380

End Page

  • 384

Volume

Issue

Place Of Publication

Abstract

  • The Sussex-Huawei Locomotion Challenge (SHL) 2021 organized at the HASCA Workshop of UbiComp 2021 is to recognize human activity by using GPS reception, GPS location, Wifi reception and GSM cell tower scans data. Compared with the previous challenge, this challenge is more difficult. In this paper, our team (NUC) summarize our submission to the competition. We propose a framework of deep learning including one-dimensional (1D) Dense CNN and Dense IndRNN networks to explore short and long-Term spatial temporal information. The location and Wifi features are extracted and normalized to feed into deep learning networks. Finally, the decision fusion is utilized to improve performance.

Publication Date

  • 2021

Publisher

Citation

  • Li, C., Li, S., Gao, Y., Guo, J., Chen, P., & Li, W. (2021). Dense CNN and IndRNN for the Sussex-Huawei Locomotion-Transportation Recognition Challenge. In UbiComp/ISWC 2021 - 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 (pp. 380-384). doi:10.1145/3460418.3479378

Scopus Eid

  • 2-s2.0-85115944374

Web Of Science Accession Number

Start Page

  • 380

End Page

  • 384

Volume

Issue

Place Of Publication