Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge
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- Lin Wang
- Wearable Technologies Lab, Sensor Technology Research Centre, University of Sussex, UK
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- Hristijan Gjoreskia
- Ss. Cyril and Methodius University, MK
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- Kazuya Murao
- College of Info. Sci. and Eng., Ritsumeikan University, Japan
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- Tsuyoshi Okita
- Kyushu Institute of Technology, Japan
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- Daniel Roggen
- Wearable Technologies Lab, Sensor Technology Research Centre, University of Sussex, UK
説明
In this paper we summarize the contributions of participants to the Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp 2018. The SHL challenge is a machine learning and data science competition, which aims to recognize eight transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial and pressure sensor data of a smartphone. We introduce the dataset used in the challenge and the protocol for the competition. We present a meta-analysis of the contributions from 19 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, two entries achieved F1 scores above 90%, eight with F1 scores between 80% and 90%, and nine between 50% and 80%.
収録刊行物
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- Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
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Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers 1521-1530, 2018-10-08
ACM
