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CapSoles: Who Is Walking on What Kind of Floor?

: Matthies, Denys J.C.; Roumen, Thijs; Kuijper, Arjan; Urban, Bodo


Association for Computing Machinery -ACM-:
MobileHCI 2017, Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services : Vienna, Austria, September 04 - 07, 2017
New York: ACM, 2017
ISBN: 978-1-4503-5075-4
Article 9, 14 S.
International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) <19, 2017, Vienna>
Fraunhofer IGD ()
Wearable computing; Capacitive sensors; Data mining; Machine learning; Input device; User interface; Individual Health; Human computer interaction (HCI); Foot Interaction; Ground Surface Detection; floor detection; User Identification; smart implicit input; insole; shoe interface

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.