research-article

Summary of the sussex-huawei locomotion-transportation recognition challenge 2020

Authors:

Hristijan Gjoreski,

Mathias Ciliberto,

Tsuyoshi Okita,

Daniel RoggenAuthors Info & Claims

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

Pages 351 - 358

https://doi.org/10.1145/3410530.3414341

Published: 12 September 2020 Publication History

Abstract

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 is to recognize eight locomotion and transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial sensor data of a smartphone in a user-independent manner with an unknown target phone position. The training data of a "train" user is available from smartphones placed at four body positions (Hand, Torso, Bag and Hips). The testing data originates from "test" users with a smartphone placed at one, but unknown, body position. We introduce the dataset used in the challenge and the protocol of the competition. We present a meta-analysis of the contributions from 15 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, one submission achieved F1 scores above 80%, three with F1 scores between 70% and 80%, seven between 50% and 70%, and four below 50%, with a latency of maximum of 5 seconds.

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Cited By

Oh SJeong HChung SLim JNoh KKostakos VKay JHoang T(2024)A Hybrid Algorithmic Pipeline for Robust Recognition of Human Locomotion: Addressing Missing Sensor ModalitiesCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678462(591-596)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678462
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 RNing TZhu YGuo SLuo HZhao F(2023)Enhancing Transportation Mode Detection using Multi-scale Sensor Fusion and Spatial-topological AttentionAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610751(534-539)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610751
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Index Terms

Summary of the sussex-huawei locomotion-transportation recognition challenge 2020
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

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Published In

cover image ACM Conferences

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

September 2020

732 pages

ISBN:9781450380768

DOI:10.1145/3410530

General Chairs:
Monica Tentori
CICESE, Mexico
,
Nadir Weibel
UC San Diego
,
Kristof Van Laerhoven
University of Siegen, Germany
,
Program Chairs:
Gregory Abowd
Georgia Tech
,
Flora Salim
RMIT, Australia

Copyright © 2020 ACM.

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Published: 12 September 2020

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UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers

September 12 - 17, 2020

Virtual Event, Mexico

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

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Cited By

Oh SJeong HChung SLim JNoh KKostakos VKay JHoang T(2024)A Hybrid Algorithmic Pipeline for Robust Recognition of Human Locomotion: Addressing Missing Sensor ModalitiesCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678462(591-596)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678462
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 RNing TZhu YGuo SLuo HZhao F(2023)Enhancing Transportation Mode Detection using Multi-scale Sensor Fusion and Spatial-topological AttentionAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610751(534-539)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610751
Mostafa SHarras KYoussef M(2023)Ubiquitous Transportation Mode Estimation using Limited Cell Tower Information2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)10.1109/VTC2023-Spring57618.2023.10200431(1-5)Online publication date: Jun-2023
https://doi.org/10.1109/VTC2023-Spring57618.2023.10200431
Zhu YLuo HGuo SZhao F(2023)DMSTL: A Deep Multi-Scale Transfer Learning Framework for Unsupervised Cross-Position Human Activity RecognitionIEEE Internet of Things Journal10.1109/JIOT.2022.320454210:1(787-800)Online publication date: 1-Jan-2023
https://doi.org/10.1109/JIOT.2022.3204542
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
Gomaa WKhamis M(2023)A perspective on human activity recognition from inertial motion dataNeural Computing and Applications10.1007/s00521-023-08863-935:28(20463-20568)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.1007/s00521-023-08863-9
Kalabakov SStankoski SKiprijanovska IAndova AReščič NJanko VGjoreski MGams MLuštrek M(2022)What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei ChallengesSensors10.3390/s2210361322:10(3613)Online publication date: 10-May-2022
https://doi.org/10.3390/s22103613
Matthes PSpringer T(2022)Selecting Resource-Efficient ML Models for Transport Mode Detection on Mobile Devices2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)10.1109/IoTaIS56727.2022.9976004(135-141)Online publication date: 24-Nov-2022
https://doi.org/10.1109/IoTaIS56727.2022.9976004
Oplenskedal MHerrmann PTaherkordi A(2022) DeepMatch2Information Systems10.1016/j.is.2021.101927108:COnline publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.is.2021.101927
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