Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge

DOI Web Site 2 Citations Open Access
  • Lin Wang
    Wearable Technologies Lab, Sensor Technology Research Centre, University of Sussex, UK
  • Hristijan Gjoreskia
    Ss. Cyril and Methodius University, MK
  • Kazuya Murao
    College of Info. Sci. and Eng., Ritsumeikan University, Japan
  • Tsuyoshi Okita
    Kyushu Institute of Technology, Japan
  • Daniel Roggen
    Wearable Technologies Lab, Sensor Technology Research Centre, University of Sussex, UK

Description

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%.

Journal

Citations (2)*help

See more

Details 詳細情報について

Report a problem

Back to top