Summary of SHL Challenge 2023: Recognizing Locomotion and Transportation Mode from GPS and Motion Sensors-Reference-Cited by-同舟云学术

Summary of SHL Challenge 2023: Recognizing Locomotion and Transportation Mode from GPS and Motion Sensors

Published:2023-10-08 Issue: Volume: Page:
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Container-title:Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing
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Author:

Wang Lin¹^ORCID,Gjoreski Hristijan²^ORCID,Ciliberto Mathias³^ORCID,Lago Paula⁴^ORCID,Murao Kazuya⁵^ORCID,Okita Tsuyoshi⁶^ORCID,Roggen Daniel³^ORCID

Affiliation:

1. Queen Mary University of London, United Kingdom

2. Ss. Cyril and Methodius University in Skopje, Macedonia, The Former Yugoslav Republic of

3. University of Sussex, United Kingdom

4. Concordia University, Canada

5. Ritsumeikan University, Japan

6. Kyushu Institute of Technology, Japan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3594739.3610758

Reference48 articles.

1. Multimodal Sensor Data Fusion and Ensemble Modeling for Human Locomotion Activity Recognition

2. [ 2 ] S. Huang , User-independent Motion and Location Analysis for Sussex-Huawei Locomotion Data . Proc. UbiComp/ISWC 2023 . [2] S. Huang, et al. User-independent Motion and Location Analysis for Sussex-Huawei Locomotion Data. Proc. UbiComp/ISWC 2023.

3. Enhanced SHL Recognition Using Machine Learning and Deep Learning Models with Multi-source Data

4. Road Network Enhanced Transportation Mode Recognition with an Ensemble Machine Learning Model

5. Enhancing XGBoost with Heuristic Smoothing for Transportation Mode and Activity Recognition

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