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Improved Feature Detection over Large Force Ranges Using History Dependent Transfer Functions
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. (Visual learning and communication)ORCID iD: 0000-0003-1032-2145
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences. (Visual learning and communication)ORCID iD: 0000-0002-4694-5611
Show others and affiliations
2009 (English)In: Third Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems, WorldHaptics 2009, IEEE , 2009, 476-481 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present a history dependent transfer function (HDTF) as a possible approach to enable improved haptic feature detection in high dynamic range (HDR) volume data. The HDTF is a multi-dimensional transfer function that uses the recent force history as a selection criterion to switch between transfer functions, thereby adapting to the explored force range. The HDTF has been evaluated using artificial test data and in a realistic application example, with the HDTF applied to haptic protein-ligand docking. Biochemistry experts performed docking tests, and expressed that the HDTF delivers the expected feedback across a large force magnitude range, conveying both weak attractive and strong repulsive protein-ligand interaction forces. Feature detection tests have been performed with positive results, indicating that the HDTF improves the ability of feature detection in HDR volume data as compared to a static transfer function covering the same range.

Place, publisher, year, edition, pages
IEEE , 2009. 476-481 p.
Keyword [en]
Haptics, Virtual Reality, Scientific Visualization
National Category
Interaction Technologies
Identifiers
URN: urn:nbn:se:liu:diva-45355DOI: 10.1109/WHC.2009.4810843Local ID: 81912ISBN: 978-1-4244-3858-7 (print)OAI: oai:DiVA.org:liu-45355DiVA: diva2:266217
Conference
Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics 2009.Salt Lake City, UT, USA, 18-20 March 2009
Projects
VisMolLS
Note

©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Petter Bivall Persson, Gunnar E. Höst, Matthew D. Cooper, Lena A. E. Tibell and Anders Ynnerman, Improved Feature Detection over Large Force Ranges Using History Dependent Transfer Functions, 2009, Third Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems, WorldHaptics 2009, 476-481. http://dx.doi.org/10.1109/WHC.2009.4810843

Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2016-05-04Bibliographically approved
In thesis
1. Touching the Essence of Life: Haptic Virtual Proteins for Learning
Open this publication in new window or tab >>Touching the Essence of Life: Haptic Virtual Proteins for Learning
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This dissertation presents research in the development and use of a multi-modal visual and haptic virtual model in higher education. The model, named Chemical Force Feedback (CFF), represents molecular recognition through the example of protein-ligand docking, and enables students to simultaneously see and feel representations of the protein and ligand molecules and their force interactions. The research efforts have been divided between educational research aspects and development of haptic feedback techniques.

The CFF model was evaluated in situ through multiple data-collections in a university course on molecular interactions. To isolate possible influences of haptics on learning, half of the students ran CFF with haptics, and the others used the equipment with force feedback disabled. Pre- and post-tests showed a significant learning gain for all students. A particular influence of haptics was found on students reasoning, discovered through an open-ended written probe where students' responses contained elaborate descriptions of the molecular recognition process.

Students' interactions with the system were analyzed using customized information visualization tools. Analysis revealed differences between the groups, for example, in their use of visual representations on offer, and in how they moved the ligand molecule. Differences in representational and interactive behaviours showed relationships with aspects of the learning outcomes.

The CFF model was improved in an iterative evaluation and development process. A focus was placed on force model design, where one significant challenge was in conveying information from data with large force differences, ranging from very weak interactions to extreme forces generated when atoms collide. Therefore, a History Dependent Transfer Function (HDTF) was designed which adapts the translation of forces derived from the data to output forces according to the properties of the recently derived forces. Evaluation revealed that the HDTF improves the ability to haptically detect features in volumetric data with large force ranges.

To further enable force models with high fidelity, an investigation was conducted to determine the perceptual Just Noticeable Difference (JND) in force for detection of interfaces between features in volumetric data. Results showed that JNDs vary depending on the magnitude of the forces in the volume and depending on where in the workspace the data is presented.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. 78 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1332
Keyword
Haptics, Educational Research, Biomolecular Education, Life Science, JND, Just Noticeable Difference, Protein-ligand Docking, Haptic docking, Visualization, Haptic Transfer Functions, Volume Data Haptics, History Dependent Transfer Function, Log file analysis, Molecular Recognition, Force Feedback, Virtual Reality
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:liu:diva-58994 (URN)978-91-7393-341-4 (ISBN)
Public defence
2010-10-01, The Dome Theater, Visualization Center C, Kungsgatan 54, Norrköping, Norrköping, 09:30 (English)
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Available from: 2010-10-12 Created: 2010-09-06 Last updated: 2016-05-04Bibliographically approved

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Bivall Persson, PetterHöst, Gunnar E.Cooper, Matthew D.Tibell, Lena A. E.Ynnerman, Anders

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Bivall Persson, PetterHöst, Gunnar E.Cooper, Matthew D.Tibell, Lena A. E.Ynnerman, Anders
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Visual Information Technology and Applications (VITA)The Institute of TechnologyDepartment of Clinical and Experimental MedicineFaculty of Health Sciences
Interaction Technologies

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