Investigating Passive Haptic Learning of Piano Songs Using Three Tactile Sensations of Vibration, Stroking and Tapping
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
Abstract
Passive Haptic Learning (PHL) is a method by which users are able to learn motor skills without paying active attention. In past research, vibration is widely applied in PHL as the signal delivered on the participant's skin. The human somatosensory system provides not only discriminative input (the perception of pressure, vibration, slip, and texture, etc.) to the brain but also an affective input (sliding, tapping and stroking, etc.). The former is often described as being mediated by low-threshold mechanosensitive (LTM) units with rapidly conducting large myelinated (Aᵬ) afferents, while the latter is mediated by a class of LTM afferents called C-tactile afferents (CTs). We investigated whether different tactile sensations (tapping, light stroking, and vibration) influence the learning effect of PHL in this work. We built three wearable systems corresponding to the three sensations respectively. 17 participants were invited to learn to play three different note sequences passively via three different systems. The subjects were then tested on their remembered note sequences after each learning session. Our results indicate that the sensations of tapping or stroking are as effective as the vibration system in passive haptic learning of piano songs, providing viable alternatives to the vibration sensations that have been used so far. We also found that participants on average made up to 1.06 errors less when using affective inputs, namely tapping or stroking. As the first work exploring the differences in multiple types of tactile sensations in PHL, we offer our design to the readers and hope they may employ our works for further research of PHL.
Details
Originalsprache | Englisch |
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Aufsatznummer | 95 |
Fachzeitschrift | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies |
Jahrgang | 7 |
Ausgabenummer | 3 |
Publikationsstatus | Veröffentlicht - 27 Sept. 2023 |
Peer-Review-Status | Ja |
Extern publiziert | Ja |
Externe IDs
Scopus | 85173263613 |
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