Data underlying the publication- SENS3: Multisensory Database of Finger-Surface Interactions and Corresponding Sensations

doi:10.4121/e7f8f6dd-b61c-42fc-bea9-5ff103cbe396.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/e7f8f6dd-b61c-42fc-bea9-5ff103cbe396
Datacite citation style:
K Balasubramanian, Jagan; L Kodak, Bence; Vardar, Yasemin (2024): Data underlying the publication- SENS3: Multisensory Database of Finger-Surface Interactions and Corresponding Sensations. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/e7f8f6dd-b61c-42fc-bea9-5ff103cbe396.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

The growing demand for natural interactions with technology underscores the importance of achieving realistic touch sensations in digital environments. Realizing this goal highly depends on comprehensive databases of finger-surface interactions, which need further development. Here, we present SENS3—www.sens3.net— an extensive open-access repository of multisensory data acquired from fifty surfaces when two participants explored them with their fingertips through static contact, pressing, tapping, and sliding. SENS3 encompasses high-fidelity visual, audio, and haptic information recorded during these interactions, including videos, sounds, contact forces, torques, positions, accelerations, skin temperature, heat flux, and surface photographs. Additionally, it incorporates thirteen participants’ psychophysical sensation ratings (rough–smooth, flat–bumpy, sticky–slippery, hot–cold, regular–irregular, fine–coarse, hard–soft, and wet–dry) while exploring these surfaces freely. Designed with an open-ended framework, SENS3 has the potential to be expanded with additional textures and participants. We anticipate that SENS3 will be valuable for advancing multisensory texture rendering, user experience development, and touch sensing in robotics.


history
  • 2024-07-10 first online, published, posted
publisher
4TU.ResearchData
funding
  • NWO veni project no. 19153 (grant code 19153) Dutch Research Council (NWO) and Huawei Technologies
organizations
TU Delft, Faculty of Mechanical Engineering, Department of Cognitive Robotics

DATA

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