Three-Year Review of the 2018–2020 SHL Challenge on Transportation and Locomotion Mode Recognition From Mobile Sensors

Lin Wang,Kazuya Murao,Mathias Ciliberto,Daniel Roggen,Paula Lago,Hristijan Gjoreski,Tsuyoshi Okita

doi:10.3389/fcomp.2021.713719

Abstract

The 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 user-independent (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.

Highlights

The mode of transportation or locomotion of a user is an important contextual information and includes things such as the knowledge of the user walking, running, riding a bicycle, taking a bus, driving a car and others (Engelbrecht et al, 2015)
Significant work has been devoted to the recognition of locomotion and transportation modes from the Global Positioning Systems (GPS) data available on smartphones
GPS data has clear advantages, such as providing exact location which can be correlated to road and rail maps in addition to providing speed and heading. They have drawbacks: they tend to be power hungry, do not work well indoors, and they often do not provide sufficiently granular information to distinguish between some modes of transportation[1]. This survey is not concerned with GPS-based recognition and we refer the interested reader to the review (Gong et al, 2014) and to recent work such as (Dabiri and Heaslip, 2018; Guo et al, 2020) on this topic

Summary

Introduction

The mode of transportation or locomotion of a user is an important contextual information and includes things such as the knowledge of the user walking, running, riding a bicycle, taking a bus, driving a car and others (Engelbrecht et al, 2015). GPS data has clear advantages, such as providing exact location which can be correlated to road and rail maps in addition to providing speed and heading They have drawbacks: they tend to be power hungry, do not work well indoors, and they often do not provide sufficiently granular information to distinguish between some modes of transportation[1]. This survey is not concerned with GPS-based recognition and we refer the interested reader to the review (Gong et al, 2014) and to recent work such as (Dabiri and Heaslip, 2018; Guo et al, 2020) on this topic

Results

Discussion

Conclusion