research-article

Smartphone location identification and transport mode recognition using an ensemble of generative adversarial networks

Authors:

Lukas Günthermann,

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 311 - 316

https://doi.org/10.1145/3410530.3414353

Published: 12 September 2020 Publication History

Abstract

We present a generative adversarial network (GAN) approach to recognising modes of transportation from smartphone motion sensor data, as part of our contribution to the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge 2020 as team noname. Our approach identifies the location where the smartphone of the test dataset is carried on the body through heuristics, after which a location-specific model is trained based on the available published data at this location. Performance on the validation data is 0.95, which we expect to be very similar on the test set, if our estimation of the location of the phone on the test set is correct. We are highly confident in this location estimation. If however it were wrong, an accuracy as low as 30% could be expected.

References

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H. Gjoreski et al.: The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics With Mobile Devices. IEEE Access 6 (2018) 42592--42604

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L. Wang et al.: Enabling reproducible research in sensor-based transportation mode recognition with the Sussex-Huawei dataset. IEEE Access 7 (2019) 10870--10891

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L. Wang et al.: Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge. In: Proc. ACM Int Joint Conf and 2018 Int Symp on Pervasive and Ubiquitous Computing and Wearable Computers, ACM (2018) 1521--1530

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Digital Library

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L. Günthermann, A. Philippides, and D. Roggen: Improving smartphone based transport mode recognition using generative adversarial networks. (in press) (2020)

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L. Günthermann, I. Simson, D. Roggen: SHL GAN (2020)

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Cited By

Huang SLyu JZhang STang RXiao HZhang YLu X(2023)A Post-processing Machine Learning for Activity Recognition Challenge with OpenStreetMap DataAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610755(557-562)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610755
Gunthermann L(2023)Application for Doctoral Consortium IUI 2023Companion Proceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581754.3584112(233-236)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581754.3584112
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
Show More Cited By

Index Terms

Smartphone location identification and transport mode recognition using an ensemble of generative adversarial networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices

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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

Huang SLyu JZhang STang RXiao HZhang YLu X(2023)A Post-processing Machine Learning for Activity Recognition Challenge with OpenStreetMap DataAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610755(557-562)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610755
Gunthermann L(2023)Application for Doctoral Consortium IUI 2023Companion Proceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581754.3584112(233-236)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581754.3584112
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
Zhao BLi SGao YLi CLi W(2020)A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity RecognitionSensors10.3390/s2023698420:23(6984)Online publication date: 7-Dec-2020
https://doi.org/10.3390/s20236984
Wang LGjoreski HCiliberto MLago PMurao KOkita TRoggen DTentori MWeibel NVan Laerhoven KAbowd GSalim F(2020)Summary of the sussex-huawei locomotion-transportation recognition challenge 2020Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410530.3414341(351-358)Online publication date: 10-Sep-2020
https://dl.acm.org/doi/10.1145/3410530.3414341

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