research-article

Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge

Authors:

Hristijan Gjoreskia,

Tsuyoshi Okita,

Daniel RoggenAuthors Info & Claims

UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

Pages 1521 - 1530

https://doi.org/10.1145/3267305.3267519

Published: 08 October 2018 Publication History

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%.

References

[1]

M. Gjoreski, et al., Applying multiple knowledge to Sussex-Huawei locomotion challenge. Proc. HASCA2018.

Digital Library

[2]

V. Janko, et al., A new frontier for activity recognition - the Sussex-Huawei locomotion challenge. Proc. HASCA2018.

Digital Library

[3]

C. Ito, et al., Application of CNN for human activity recognition with FFT spectrogram of acceleration and gyro sensors. Proc. HASCA2018.

Digital Library

[4]

P. Widhalm, et al., Top in the lab, flop in the field? Evaluation of a sensor-based travel activity classifier with the SHL dataset. Proc. HASCA2018.

Digital Library

[5]

A. D. Antar, et al., A comparative approach to classification of locomotion and transportation modes using smartphone sensor data. Proc. HASCA2018.

Digital Library

[6]

A Akbari, et al., Hierarchical signal segmentation and classification for accurate activity recognition. Proc. HASCA2018.

Digital Library

[7]

H. Matsuyama, et al., Short segment random forest with post processing using label constraint for SHL challenge. Proc. HASCA2018.

Digital Library

[8]

Y. Nakamura, et al., Multi-stage activity inference for locomotion and transportation analytics of mobile users. Proc. HASCA2018.

Digital Library

[9]

Y. Yuki, et al., Activity Recognition using Dual-ConvLSTM Extracting Local and Global Features for SHL Challenge. Proc. HASCA2018.

Digital Library

[10]

J. Wu, et al., A decision level fusion and signal analysis technique for activity segmentation and recognition on smart phones. Proc. HASCA2018.

Digital Library

[11]

S. S. Saha, et al., Supervised and semi-supervised classifiers for locomotion analysis. Proc. HASCA2018.

Digital Library

[12]

A. Osmani, et al., Hybrid and convolutional neural networks for locomotion recognition. Proc. HASCA2018.

Digital Library

[13]

J. V. Jeyakumar, et al., Deep convolutional bidirectional LSTM based transportation mode recognition. Proc. HASCA2018.

Digital Library

[14]

T. B. Zahid, et al., A fast resource efficient method for human action recognition. Proc. HASCA2018.

Digital Library

[15]

J. H. Choi, et al., Confidence-based deep multimodal fusion for activity recognition. Proc. HASCA2018.

Digital Library

[16]

S. Li, et al., Smartphone-sensors based activity recognition using IndRNN. Proc. HASCA2018.

Digital Library

[17]

T. Yamaguchi, Sussex-Huawei locomotion challenge using XGBoost. (not included in Proc. HASCA2018).

[18]

K. Akamine, et al., SHL recognition challenge: Team TK-2 - combining results of multisize instances. Proc. HASCA2018.

Digital Library

[19]

M. Sloma, et al., Activity recognition by classification with time stabilization for the Sussex-Huawei locomotion challenge. Proc. HASCA2018.

Digital Library

[20]

L. Wang, et al., Benchmarking the SHL recognition challenge with classical and deep-learning pipelines. Proc. HASCA2018.

Digital Library

[21]

H. Gjoreski, et al., The university of Sussex-Huawei transportation-locomotion dataset for multimodal analytics with mobile devices, IEEE Access. 2018.

[22]

L. Wang, et al. Enabling reproducible research in sensor-based transportation mode recognition with the Sussex-Huawei dataset. under review.

[23]

M. Ciliberto, et al. High reliability Android application for multi-device multimodal mobile data acquisition and annotation. Proc. ENSS2017.

Digital Library

[24]

H. Xia, et al., Using smart phone sensors to detect transportation modes. Sensors, (2014), 20843--20865.

[25]

M. C. Yu, et al., Big data small footprint: the design of a low-power classifier for detecting transportation modes. Proc. VLDB Endowment (2014), 1429--1440.

Digital Library

Cited By

Ge JXu GLu JXu XLi LMeng X(2024)SensorNet: An Adaptive Attention Convolutional Neural Network for Sensor Feature LearningSensors10.3390/s2411327424:11(3274)Online publication date: 21-May-2024
https://doi.org/10.3390/s24113274
Siargkas CPapapanagiotou VDelopoulos A(2024)Transportation Mode Recognition Based on Low-Rate Acceleration and Location Signals With an Attention-Based Multiple-Instance Learning NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.338783425:10(14376-14388)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3387834
Wang LGjoreski HCiliberto MLago PMurao KOkita TRoggen D(2023)Summary of SHL Challenge 2023: Recognizing Locomotion and Transportation Mode from GPS and Motion SensorsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610758(575-585)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610758
Show More Cited By

Index Terms

Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge
1. Computing methodologies
  1. Machine learning
2. Human-centered computing

Recommendations

Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2019
UbiComp/ISWC '19 Adjunct: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers

In this paper we summarize the contributions of participants to the Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge organized at the HASCA Workshop of Ubi-Comp 2019. The goal of this machine learning/data science challenge is to ...
Locomotion and Transportation Mode Recognition from GPS and Radio Signals: Summary of SHL Challenge 2021
UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers

In this paper we summarize the contributions of participants to the fourth Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp/ISWC 2021. The goal of this machine learning/data science ...
Summary of the sussex-huawei locomotion-transportation recognition challenge 2020
UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

In this paper we summarize the contributions of participants to the third Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp/ISWC 2020. The goal of this machine learning/data science challenge ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers

October 2018

1881 pages

ISBN:9781450359665

DOI:10.1145/3267305

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
University of Florida: University of Florida
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

UbiComp '18

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 8 - 12, 2018

Singapore, Singapore

Acceptance Rates

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

68
Total Citations
View Citations
504
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)2

Reflects downloads up to 10 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ge JXu GLu JXu XLi LMeng X(2024)SensorNet: An Adaptive Attention Convolutional Neural Network for Sensor Feature LearningSensors10.3390/s2411327424:11(3274)Online publication date: 21-May-2024
https://doi.org/10.3390/s24113274
Siargkas CPapapanagiotou VDelopoulos A(2024)Transportation Mode Recognition Based on Low-Rate Acceleration and Location Signals With an Attention-Based Multiple-Instance Learning NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.338783425:10(14376-14388)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3387834
Wang LGjoreski HCiliberto MLago PMurao KOkita TRoggen D(2023)Summary of SHL Challenge 2023: Recognizing Locomotion and Transportation Mode from GPS and Motion SensorsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610758(575-585)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610758
Chen XZhong XZhou SFeng Y(2023)Recognize Locomotion and Transportation Modes from Wi-Fi Traces via Lightweight Models2023 International Conference on Future Communications and Networks (FCN)10.1109/FCN60432.2023.10544151(1-6)Online publication date: 17-Dec-2023
https://doi.org/10.1109/FCN60432.2023.10544151
Feng YZhong XZhou SChen X(2023)Communication Scene Recognition Method Based on Multi Phone Sensors and Deep Learning2023 International Conference on Future Communications and Networks (FCN)10.1109/FCN60432.2023.10544117(1-6)Online publication date: 17-Dec-2023
https://doi.org/10.1109/FCN60432.2023.10544117
Moreau HVassilev AChen L(2022)The Devil is in the Details: An Efficient Convolutional Neural Network for Transport Mode DetectionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.311094923:8(12202-12212)Online publication date: Aug-2022
https://doi.org/10.1109/TITS.2021.3110949
Bian SYuan SRey VLukowicz P(2022)Using Human Body Capacitance Sensing to Monitor Leg Motion Dominated Activities with a Wrist Worn DeviceSensor- and Video-Based Activity and Behavior Computing10.1007/978-981-19-0361-8_5(81-94)Online publication date: 4-May-2022
https://doi.org/10.1007/978-981-19-0361-8_5
Rakib Sayem FSheikh MAhad M(2022)Bento Packaging Activity Recognition Based on Statistical FeaturesSensor- and Video-Based Activity and Behavior Computing10.1007/978-981-19-0361-8_13(207-216)Online publication date: 4-May-2022
https://doi.org/10.1007/978-981-19-0361-8_13
Günthermann LWang LSimpson IPhilippides ARoggen D(2022)Adversarial Learning in Accelerometer Based Transportation and Locomotion Mode RecognitionGenerative Adversarial Learning: Architectures and Applications10.1007/978-3-030-91390-8_10(205-232)Online publication date: 7-Feb-2022
https://doi.org/10.1007/978-3-030-91390-8_10
Kojima RLegaspi RMishima YWada S(2021)Classical machine learning and deep neural network ensemble model for GPS-based activity recognitionAdjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460418.3479386(369-373)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1145/3460418.3479386
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents