Wang, Lin, Gjoreski, Hristijan, Murao, Kazuya, Okita, Tsuyoshi and Roggen, Daniel Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge. HASCA Workshop at Ubicomp 2018, Singapore, 8-12 October, 2018. Published in: Murao, Kazuya, Enokibori, Yu, Gjoreski, Hristijan, Lago, Paula, Okita, Tsuyoshi, Siirtola, Pekka, Hiroi, Kei and Scholl, Philipp, (eds.) Ubicomp 2018 Adjunct proceeings. Association for Computing Machinery ISBN 978-1-4503-5966-5/18/10 (Accepted)

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Abstract

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%.

Item Type:	Conference Proceedings
Keywords:	Activity recognition; Deep learning; Machine learning; Mobile sensing; Transportation mode recognition
Schools and Departments:	School of Engineering and Informatics > Engineering and Design
Research Centres and Groups:	Sensor Technology Research Centre
Subjects:	Q Science > Q Science (General) > Q0300 Cybernetics Q Science > QA Mathematics > QA0075 Electronic computers. Computer science > QA0076.9.A-Z Other topics, A-Z > QA0076.9.B45 Big data Q Science > QA Mathematics > QA0276 Mathematical statistics Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Related URLs:	http://ubicomp.org/ubicomp2018/
Depositing User:	Daniel Roggen
Date Deposited:	05 Sep 2018 10:54
Last Modified:	05 Sep 2018 10:54
URI:	http://srodev.sussex.ac.uk/id/eprint/78511

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