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Cited by 191

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Vision-based sensor for real-time measuring of surface traction fields

Kamiyama, K., Vlack, K., Mizota, T., Kajimoto, H., Kawakami, K., Tachi, S.·2005·IEEE Computer Graphics and Applications

Abstract

The desire to reproduce and expand the human senses drives innovations in sensor technology. Conversely, human-interface research aims to allow people to interact with machines as if they were natural objects in a cybernetic, human-oriented way. We wish to unite the two paradigms with a haptic sensor as versatile as the sense of touch and developed for a dual purpose: to improve the robotic capability to interact with the physical world, and to improve the human capability to interact with the virtual world for emerging applications with a heightened sense of presence. We designed a sensor, dubbed GelForce, that acts as a practical tool in both conventional and novel desktop applications using common consumer hardware. By measuring a surface traction field, the GelForce tactile sensor can represent the magnitude and direction of force applied to the skin's surface using computer vision. This article is available with a short video documentary on CD-ROM.

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Metadata

Authors
Kamiyama, K., Vlack, K., Mizota, T., Kajimoto, H., Kawakami, K., & Tachi, S.
Venue
IEEE Computer Graphics and Applications
Published
2005-01
Volume
25
Issue
1
Pages
68-75
ISSN
0272-1716 (ISSN-L)
OpenAlex
W2142736275
Cited by
191
References
0
FWCI
12.56
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Vision-based sensor for real-time measuring of surface traction fields. https://app.cochat.ai/papers/10.1109/mcg.2005.27
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Kamiyama, K., Vlack, K., Mizota, T., Kajimoto, H., Kawakami, K., & Tachi, S. (2005). Vision-based sensor for real-time measuring of surface traction fields. IEEE Computer Graphics and Applications. https://doi.org/10.1109/mcg.2005.27

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.