Matthies, Denys J.C.Denys J.C.MatthiesRoumen, ThijsThijsRoumenKuijper, ArjanArjanKuijperUrban, BodoBodoUrban2022-03-132022-03-132017https://publica.fraunhofer.de/handle/publica/39785810.1145/3098279.3098545Foot 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.enWearable computingCapacitive sensorsData miningMachine learningInput deviceUser interfaceIndividual HealthHuman computer interaction (HCI)Foot InteractionGround Surface Detectionfloor detectionUser Identificationsmart implicit inputinsoleshoe interface006conference paper