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CapSoles: Who Is Walking on What Kind of Floor?

Matthies, Denys J. C. and Roumen, Thijs and Kuijper, Arjan and Urban, Bodo (2017):
CapSoles: Who Is Walking on What Kind of Floor?
In: MobileHCI '17: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 1-14,
New York, Vienna, Austria, September 04-07, 2017, ISBN 978-1-4503-5075-4,
DOI: 10.1145/3098279.3098545,
[Conference or Workshop Item]

Abstract

Foot interfaces, such as pressure-sensitive insoles, still yield unused potential such as for implicit interaction. In this paper, we introduce CapSoles, enabling smart insoles to implicitly identify who is walking on what kind of floor. Our insole prototype relies on capacitive sensing and is able to sense plantar pressure distribution underneath the foot, plus a capacitive ground coupling effect. By using machine-learning algorithms, we evaluated the identification of 13 users, while walking, with a confidence of ~95% after a recognition delay of ~1s. Once the user's gait is known, again we can discover irregularities in gait plus a varying ground coupling. While both effects in combination are usually unique for several ground surfaces, we demonstrate to distinguish six kinds of floors, which are sand, lawn, paving stone, carpet, linoleum, and tartan with an average accuracy of ~82%. Moreover, we demonstrate the unique effects of wet and electrostatically charged surfaces.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Matthies, Denys J. C. and Roumen, Thijs and Kuijper, Arjan and Urban, Bodo
Title: CapSoles: Who Is Walking on What Kind of Floor?
Language: English
Abstract:

Foot interfaces, such as pressure-sensitive insoles, still yield unused potential such as for implicit interaction. In this paper, we introduce CapSoles, enabling smart insoles to implicitly identify who is walking on what kind of floor. Our insole prototype relies on capacitive sensing and is able to sense plantar pressure distribution underneath the foot, plus a capacitive ground coupling effect. By using machine-learning algorithms, we evaluated the identification of 13 users, while walking, with a confidence of ~95% after a recognition delay of ~1s. Once the user's gait is known, again we can discover irregularities in gait plus a varying ground coupling. While both effects in combination are usually unique for several ground surfaces, we demonstrate to distinguish six kinds of floors, which are sand, lawn, paving stone, carpet, linoleum, and tartan with an average accuracy of ~82%. Moreover, we demonstrate the unique effects of wet and electrostatically charged surfaces.

Title of Book: MobileHCI '17: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services
Place of Publication: New York
ISBN: 978-1-4503-5075-4
Uncontrolled Keywords: Wearable computing, Capacitive sensors, Data mining, Machine learning, Input devices, User interfaces
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Location: Vienna, Austria
Event Dates: September 04-07, 2017
Date Deposited: 04 May 2020 08:51
DOI: 10.1145/3098279.3098545
Official URL: https://doi.org/10.1145/3098279.3098545
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