research-article

Applying Multiple Knowledge to Sussex-Huawei Locomotion Challenge

Authors:

Martin Gjoreski,

Nina Reščič,

Mitja Luštrek,

Gašper Slapničar,

Matjaž GamsAuthors 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 1488 - 1496

https://doi.org/10.1145/3267305.3267515

Published: 08 October 2018 Publication History

Abstract

In recent years, activity recognition (AR) has become prominent in ubiquitous systems. Following this trend, the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge provides a unique opportunity for researchers to test their AR methods against a common, real-life and large-scale benchmark. The goal of the challenge is to recognize eight everyday activities including transit. Our team, JSI-Deep, utilized an AR approach based on combining multiple machine-learning methods following the principle of multiple knowledge. We first created several base learners using classical and deep learning approaches, then integrated them into an ensemble, and finally refined the ensemble's predictions by smoothing. On the internal test data, the approach achieved 96% accuracy, which is a significant leap over the baseline 60%.

References

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H. Gjoreski, M. Ciliberto, L. Wang, F. J. O. Morales, S. Mekki, S. Valentin, D. Roggen. The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics with Mobile Devices. IEEE Access, 2018, {In Press}.

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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
Deng JXu JSun ZLi DGuo HZhang YLu X(2023)Enhancing Locomotion Recognition with Specialized Features and Map Information via XGBoostAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610754(551-556)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610754
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Index Terms

Applying Multiple Knowledge to Sussex-Huawei Locomotion Challenge
1. Information systems
  1. Information systems applications

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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]

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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

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 08 October 2018

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UbiComp '18

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SIGMOBILE
University of Florida
SIGCHI

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

October 8 - 12, 2018

Singapore, Singapore

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

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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
Deng JXu JSun ZLi DGuo HZhang YLu X(2023)Enhancing Locomotion Recognition with Specialized Features and Map Information via XGBoostAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610754(551-556)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610754
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
Tian YHettiarachchi DKamijo S(2022)Transportation Mode Detection Combining CNN and Vision Transformer with Sensors Recalibration Using Smartphone Built-In SensorsSensors10.3390/s2217645322:17(6453)Online publication date: 26-Aug-2022
https://doi.org/10.3390/s22176453
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
Janko VLuštrek M(2021)A General Framework for Making Context-Recognition Systems More Energy EfficientSensors10.3390/s2103076621:3(766)Online publication date: 24-Jan-2021
https://doi.org/10.3390/s21030766
Akbari AJafari R(2021)Transition-Aware Detection of Modes of Locomotion and Transportation Through Hierarchical SegmentationIEEE Sensors Journal10.1109/JSEN.2020.302310921:3(3301-3313)Online publication date: 1-Feb-2021
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Compagnon PLefebvre GDuffner SGarcia C(2021)Sequence Metric Learning as Synchronization of Recurrent Neural Networks2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533627(1-8)Online publication date: 2021
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Moreau HVassilev AChen L(2021)Data Fusion for Deep Learning on Transport Mode Detection: A Case StudyProceedings of the 22nd Engineering Applications of Neural Networks Conference10.1007/978-3-030-80568-5_12(141-152)Online publication date: 1-Jul-2021
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