Article

Barrier Detection Using Sensor Data from Unimpaired Pedestrians

Authors:

Akihiro Miyata,

Tongshun WangAuthors Info & Claims

Universal Access in Human-Computer Interaction. Virtual, Augmented, and Intelligent Environments : 12th International Conference, UAHCI 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018, Proceedings, Part II

Pages 308 - 319

https://doi.org/10.1007/978-3-319-92052-8_24

Published: 15 July 2018 Publication History

Abstract

There are several barriers (e.g., steps, slopes) that hinder the free movement of impaired people, both indoors and outdoors. Existing approaches for detecting barriers have an accuracy/coverage trade-off problem. For example, approaches that use a wheelchair with an accelerometer cannot detect barriers in areas that wheelchair users have not gone through. However, approaches that try to detect barriers from street images on the Internet fail to increase the accuracy of barrier detection because of occlusions that obscure the surface of the road. To address this problem, we propose a barrier detection approach that uses a machine learning model trained with acceleration data acquired from smartphones of able-bodied pedestrians. This idea uses pedestrians as sensor nodes for detecting barriers. This approach enables us to collect barrier information for a large area with high accuracy without any special investigators or devices. The results of the evaluation using acceleration data of pedestrians show that our method could identify barriers accurately by using hand-crafted features. Furthermore, we also clarify that the identification accuracy improves when features auto-generated by deep learning (Denoising Autoencoders) are used.

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Cited By

Miyata AOkugawa KMurayama YFuruta AOchiai KMurayama Y(2023)Case Study: In-the-Field Accessibility Information Collection Using GamificationProceedings of the 20th International Web for All Conference10.1145/3587281.3587288(66-74)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3587281.3587288
Miyata AOkugawa KYamato YMaeda TMurayama YAibara MFuruichi MMurayama Y(2021)A Crowdsourcing Platform for Constructing Accessibility Maps Supporting Multiple Participation ModesExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3451688(1-6)Online publication date: 8-May-2021
https://dl.acm.org/doi/10.1145/3411763.3451688
Miyata AOkugawa KYamato YAibara MFuruichi MMurayama Y(2020)BScanner: A Crowdsourcing Platform for Constructing Accessibility Maps to Support Multiple Participation TypesProceedings of the 32nd Australian Conference on Human-Computer Interaction10.1145/3441000.3441062(666-670)Online publication date: 2-Dec-2020
https://dl.acm.org/doi/10.1145/3441000.3441062
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Barrier Detection Using Sensor Data from Unimpaired Pedestrians

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cover image Guide Proceedings

Universal Access in Human-Computer Interaction. Virtual, Augmented, and Intelligent Environments : 12th International Conference, UAHCI 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018, Proceedings, Part II

Jul 2018

593 pages

ISBN:978-3-319-92051-1

DOI:10.1007/978-3-319-92052-8

Editors:
Margherita Antona
Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece
,
Constantine Stephanidis
University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

© Springer International Publishing AG, part of Springer Nature 2018.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 15 July 2018

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Cited By

Miyata AOkugawa KMurayama YFuruta AOchiai KMurayama Y(2023)Case Study: In-the-Field Accessibility Information Collection Using GamificationProceedings of the 20th International Web for All Conference10.1145/3587281.3587288(66-74)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3587281.3587288
Miyata AOkugawa KYamato YMaeda TMurayama YAibara MFuruichi MMurayama Y(2021)A Crowdsourcing Platform for Constructing Accessibility Maps Supporting Multiple Participation ModesExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3451688(1-6)Online publication date: 8-May-2021
https://dl.acm.org/doi/10.1145/3411763.3451688
Miyata AOkugawa KYamato YAibara MFuruichi MMurayama Y(2020)BScanner: A Crowdsourcing Platform for Constructing Accessibility Maps to Support Multiple Participation TypesProceedings of the 32nd Australian Conference on Human-Computer Interaction10.1145/3441000.3441062(666-670)Online publication date: 2-Dec-2020
https://dl.acm.org/doi/10.1145/3441000.3441062
Kobayashi SKatsurada RHasegawa T(2019)Estimation of Sidewalk Surface Type with a SmartphoneProceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City10.1145/3377170.3377263(497-502)Online publication date: 20-Dec-2019
https://dl.acm.org/doi/10.1145/3377170.3377263

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