Matthies, Denys J. C. ; Roumen, Thijs ; Kuijper, Arjan ; Urban, Bodo (2017)
CapSoles: Who Is Walking on What Kind of Floor?
Vienna, Austria (September 04-07, 2017)
doi: 10.1145/3098279.3098545
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Matthies, Denys J. C. ; Roumen, Thijs ; Kuijper, Arjan ; Urban, Bodo |
| Art des Eintrags: | Bibliographie |
| Titel: | CapSoles: Who Is Walking on What Kind of Floor? |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Ort: | New York |
| Buchtitel: | MobileHCI '17: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services |
| Veranstaltungsort: | Vienna, Austria |
| Veranstaltungsdatum: | September 04-07, 2017 |
| DOI: | 10.1145/3098279.3098545 |
| URL / URN: | https://doi.org/10.1145/3098279.3098545 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Wearable computing, Capacitive sensors, Data mining, Machine learning, Input devices, User interfaces |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 04 Mai 2020 08:51 |
| Letzte Änderung: | 04 Mai 2020 08:51 |
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