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  User-based evaluation of data-driven haptic rendering

Höver, R., Di Luca, M., & Harders, M. (2010). User-based evaluation of data-driven haptic rendering. ACM Transactions on Applied Perception, 8(1): 7, pp. 1-23. doi:10.1145/1857893.1857900.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BDE2-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-6AA2-0
Genre: Journal Article

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 Creators:
Höver, R, Author
Di Luca, M1, 2, 3, Author              
Harders, M, Author
Affiliations:
1Research Group Multisensory Perception and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497806              
2Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In this article, the data-driven haptic rendering approach presented in our earlier work is assessed. The approach relies on recordings from real objects from which a data-driven model is derived that captures the haptic properties of the object. We conducted two studies. In the first study, the Just Noticeable Difference (JND) for small forces, as encountered in our set-up, was determined. JNDs were obtained both for active and passive user interaction. A conservative threshold curve was derived that was then used to guide the model generation in the second study. The second study examined the achievable rendering fidelity for two objects with different stiffnesses. Subjects directly compared data-driven virtual feedback with the real objects. Results indicated that it is crucial to include dynamic material effects to achieve haptic feedback that cannot be distinguished from real objects. Results also showed that the fidelity is considerably decreased for stiffer objects due to limits of the display hardware.

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 Dates: 2010-10
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1145/1857893.1857900
BibTex Citekey: 6084
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Title: ACM Transactions on Applied Perception
Source Genre: Journal
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Publ. Info: New York, NY : Association for Computing Machinery
Pages: - Volume / Issue: 8 (1) Sequence Number: 7 Start / End Page: 1 - 23 Identifier: ISSN: 1544-3558
CoNE: https://pure.mpg.de/cone/journals/resource/111056648028200