fcomp-03-713719.pdf (3.28 MB)
Three-year review of the 2018–2020 SHL challenge on transportation and locomotion mode recognition from mobile sensors
journal contribution
posted on 2023-06-10, 00:54 authored by Lin Wang, Hristijan Gjoreski, Mathias Ciliberto, Paula Lago, Kazuya Murao, Tsuyoshi Okita, Daniel RoggenDaniel RoggenThe Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenges aim to advance and capture the state-of-the-art in locomotion and transportation mode recognition from smartphone motion (inertial) sensors. The goal of this series of machine learning and data science challenges was to recognize eight locomotion and transportation activities (Still, Walk, Run, Bus, Car, Train, Subway). The three challenges focused on time-independent (SHL 2018), position-independent (SHL 2019) and userindependent (SHL 2020) evaluations, respectively. Overall, we received 48 submissions (out of 93 teams who registered interest) involving 201 scientists over the three years. The survey captures the state-of-the-art through a meta-analysis of the contributions to the three challenges, including approaches, recognition performance, computational requirements, software tools and frameworks used. It was shown that state-of-the-art methods can distinguish with relative ease most modes of transportation, although the differentiating between subtly distinct activities, such as rail transport (Train and Subway) and road transport (Bus and Car) still remains challenging. We summarize insightful methods from participants that could be employed to address practical challenges of transportation mode recognition, for instance, to tackle over-fitting, to employ robust representations, to exploit data augmentation, and to exploit smart post-processing techniques to improve performance. Finally, we present baseline results to compare the three challenges with a unified recognition pipeline and decision window length.
Funding
HumanE-AI NET; G3022; EUROPEAN UNION
History
Publication status
- Published
File Version
- Published version
Journal
Frontiers in Computer ScienceISSN
2624-9898Publisher
Frontiers MediaExternal DOI
Volume
3Page range
1-24Pages
24.0Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes