Freeform User Interfaces for Graphical Computing

報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専

In what sense can instruments and bodies be said to form spaces?

My recent work is an exploration of the physical and conceptual mechanisms that interface people with instruments. Central to this investigation is a conception of the performer/instrument assemblage as a symbiosis of two parallel and interdependent systems: one – the performer – moves through space established by the other – the instrument. Each system possesses its own intrinsic properties and characteristics; each possesses capacities to affect and be affected by one another. The music emanates from this contiguous interaction. Instrument surface is understood as a compositional resource itself, a topological façade, defined by ordinal distances, that guides gestures along its contours. Within these fluctuating constellations of spatial coordinates, I consider all the relevant ways a body can move, and establish some general combinatory rules that inform the convergence of forces within the body. The traditional subjects of compositional contemplation such as form, duration, dynamic, etc. are not attributing features to the work per se but emerge as results from spatiotemporal relations of (bodily) movement’s correspondence with (instrumental) surface and mechanism. This liberation of movement is understood as a liberation of timbre, and the inherent indeterminacy of this relationship is embraced. As such, I would hypothesize that sound is, to an extent, freed from the subtractive tendencies of perception that might otherwise subvert it into generalized typological categories. Once liberated from the imagination, sound can bypass the brain and directly engage the nervous system

Updating During Lateral Movement Using Visual and Non-Visual Motion Cues

Spatial updating, the ability to track egocentric positions of surrounding objects during self-motion, is fundamental to navigating around the world. Past studies show people make systematic errors when updating after linear self-motion. To determine the source of these errors, I measured errors in remembered target position with and without passive lateral movements. I also varied the visual (Oculus Rift) and physical (motion-platform) self-motion feedback. In general, people remembered targets as less eccentric with greater underestimations for more eccentric targets. They could use physical cues for updating, but they made larger errors than when they had only visual cues. Visual motion cues alone were enough to produce updating, and physical cues were not needed when visual cues were available. Also, people remembered the targets within the range of movement as closer to the position they were perceived before moving. However, individual perceived distance of the target did not affect their updating

I walk, therefore I am: a multidimensional study on the influence of the locomotion method upon presence in virtual reality

[EN] A defining virtual reality (VR) metric is the sense of presence, a complex, multidimensional psychophysical construct that represents how intense is the sensation of actually being there, inside the virtual environment (VE), forgetting how technology mediates the experience. Our paper explores how locomotion influences presence, studying two different ways of artificial movement along the VE: walking-in-place (through head bobbing detection) and indirect walking (through touchpad). To evaluate that influence, a narrative-neutral maze was created, from where 41 participants (N=41) had to escape. Measuring presence is a controversial topic since there is not a single, objective measure but a wide range of metrics depending on the different theoretical basis. For this reason, we have used for the first time, representative metrics from all three traditional dimensions of presence: subjective presence (SP) (self-reported through questionnaires), behavioral presence (BP) (obtained from unconscious reactions while inside the VE), and physiological presence (PP) [usually measured using heart rate or electrodermal activity (EDA)]. SP was measured with the ITC-SOPI questionnaire, BP by collecting the participants' reactions, and PP by using a bracelet that registered EDA. The results show two main findings: (i) There is no correlation between the different presence metrics. This opens the door to a simpler way of measuring presence in an objective, reliable way. (ii) There is no significant difference between the two locomotion techniques for any of the three metrics, which shows that the authenticity of VR does not rely on how you move within the VE.Soler-Domínguez, JL.; Juan-Ripoll, CD.; Contero, M.; Alcañiz Raya, ML. (2020). I walk, therefore I am: a multidimensional study on the influence of the locomotion method upon presence in virtual reality. Journal of Computational Design and Engineering. 7(5):577-590. https://doi.org/10.1093/jcde/qwaa040S57759075Baños, R. M., Botella, C., Garcia-Palacios, A., Villa, H., Perpiña, C., & Alcañiz, M. (2000). Presence and Reality Judgment in Virtual Environments: A Unitary Construct? CyberPsychology & Behavior, 3(3), 327-335. doi:10.1089/10949310050078760Biocca, F. (1992). Will Simulation Sickness Slow Down the Diffusion of Virtual Environment Technology? Presence: Teleoperators and Virtual Environments, 1(3), 334-343. doi:10.1162/pres.1992.1.3.334Biocca, F., Harms, C., & Burgoon, J. K. (2003). Toward a More Robust Theory and Measure of Social Presence: Review and Suggested Criteria. Presence: Teleoperators and Virtual Environments, 12(5), 456-480. doi:10.1162/105474603322761270Boletsis, C. (2017). The New Era of Virtual Reality Locomotion: A Systematic Literature Review of Techniques and a Proposed Typology. Multimodal Technologies and Interaction, 1(4), 24. doi:10.3390/mti1040024Boletsis, C., & Cedergren, J. E. (2019). VR Locomotion in the New Era of Virtual Reality: An Empirical Comparison of Prevalent Techniques. Advances in Human-Computer Interaction, 2019, 1-15. doi:10.1155/2019/7420781Bowman, D. A., Koller, D., & Hodges, L. F. (1998). A methodology for the evaluation of travel techniques for immersive virtual environments. Virtual Reality, 3(2), 120-131. doi:10.1007/bf01417673Bozgeyikli, E., Raij, A., Katkoori, S., & Dubey, R. (2016). Point & Teleport Locomotion Technique for Virtual Reality. Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play. doi:10.1145/2967934.2968105Bozgeyikli, E., Raij, A., Katkoori, S., & Dubey, R. (2019). Locomotion in virtual reality for room scale tracked areas. International Journal of Human-Computer Studies, 122, 38-49. doi:10.1016/j.ijhcs.2018.08.002BRESLOW, N. (1970). A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship. Biometrika, 57(3), 579-594. doi:10.1093/biomet/57.3.579Chertoff, D. B., Goldiez, B., & LaViola, J. J. (2010). Virtual Experience Test: A virtual environment evaluation questionnaire. 2010 IEEE Virtual Reality Conference (VR). doi:10.1109/vr.2010.5444804Cohen, J. (1992). Statistical Power Analysis. Current Directions in Psychological Science, 1(3), 98-101. doi:10.1111/1467-8721.ep10768783Critchley, H. D. (2002). Review: Electrodermal Responses: What Happens in the Brain. The Neuroscientist, 8(2), 132-142. doi:10.1177/107385840200800209Hale, K. S., & Stanney, K. M. (Eds.). (2014). Handbook of Virtual Environments. doi:10.1201/b17360Larsson, P., Västfjäll, D., & Kleiner, M. (2001). The Actor-Observer Effect in Virtual Reality Presentations. CyberPsychology & Behavior, 4(2), 239-246. doi:10.1089/109493101300117929Lee, K. M. (2004). Presence, Explicated. Communication Theory, 14(1), 27-50. doi:10.1111/j.1468-2885.2004.tb00302.xLessiter, J., Freeman, J., Keogh, E., & Davidoff, J. (2001). A Cross-Media Presence Questionnaire: The ITC-Sense of Presence Inventory. Presence: Teleoperators and Virtual Environments, 10(3), 282-297. doi:10.1162/105474601300343612Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association, 62(318), 399-402. doi:10.1080/01621459.1967.10482916Mantovani, G., & Riva, G. (1999). «Real» Presence: How Different Ontologies Generate Different Criteria for Presence, Telepresence, and Virtual Presence. Presence: Teleoperators and Virtual Environments, 8(5), 540-550. doi:10.1162/105474699566459Meehan, M., Razzaque, S., Insko, B., Whitton, M., & Brooks, F. P. (2005). Review of Four Studies on the Use of Physiological Reaction as a Measure of Presence in StressfulVirtual Environments. Applied Psychophysiology and Biofeedback, 30(3), 239-258. doi:10.1007/s10484-005-6381-3Peck, T. C., Fuchs, H., & Whitton, M. C. (2011). An evaluation of navigational ability comparing Redirected Free Exploration with Distractors to Walking-in-Place and joystick locomotio interfaces. 2011 IEEE Virtual Reality Conference. doi:10.1109/vr.2011.5759437Riva, G., Wiederhold, B. K., & Mantovani, F. (2019). Neuroscience of Virtual Reality: From Virtual Exposure to Embodied Medicine. Cyberpsychology, Behavior, and Social Networking, 22(1), 82-96. doi:10.1089/cyber.2017.29099.griSanchez-Vives, M. V., & Slater, M. (2005). From presence to consciousness through virtual reality. Nature Reviews Neuroscience, 6(4), 332-339. doi:10.1038/nrn1651Sano, A., Picard, R. W., & Stickgold, R. (2014). Quantitative analysis of wrist electrodermal activity during sleep. International Journal of Psychophysiology, 94(3), 382-389. doi:10.1016/j.ijpsycho.2014.09.011Schloerb, D. W. (1995). A Quantitative Measure of Telepresence. Presence: Teleoperators and Virtual Environments, 4(1), 64-80. doi:10.1162/pres.1995.4.1.64Schubert, T., Friedmann, F., & Regenbrecht, H. (2001). The Experience of Presence: Factor Analytic Insights. Presence: Teleoperators and Virtual Environments, 10(3), 266-281. doi:10.1162/105474601300343603Schuemie, M. J., van der Straaten, P., Krijn, M., & van der Mast, C. A. P. G. (2001). Research on Presence in Virtual Reality: A Survey. CyberPsychology & Behavior, 4(2), 183-201. doi:10.1089/109493101300117884Sheridan, T. B. (1992). Musings on Telepresence and Virtual Presence. Presence: Teleoperators and Virtual Environments, 1(1), 120-126. doi:10.1162/pres.1992.1.1.120Sheridan, T. B. (1996). Further Musings on the Psychophysics of Presence. Presence: Teleoperators and Virtual Environments, 5(2), 241-246. doi:10.1162/pres.1996.5.2.241Slater, M. (2004). How Colorful Was Your Day? Why Questionnaires Cannot Assess Presence in Virtual Environments. Presence: Teleoperators and Virtual Environments, 13(4), 484-493. doi:10.1162/1054746041944849Slater, M., & Steed, A. (2000). A Virtual Presence Counter. Presence: Teleoperators and Virtual Environments, 9(5), 413-434. doi:10.1162/105474600566925Slater, M., & Usoh, M. (1993). Representations Systems, Perceptual Position, and Presence in Immersive Virtual Environments. Presence: Teleoperators and Virtual Environments, 2(3), 221-233. doi:10.1162/pres.1993.2.3.221SLATER, M., USOH, M., & STEED, A. (1994). STEPS AND LADDERS IN VIRTUAL REALITY. Virtual Reality Software and Technology. doi:10.1142/9789814350938_0005Slater, M., Steed, A., & Usoh, M. (1995). The Virtual Treadmill: A Naturalistic Metaphor for Navigation in Immersive Virtual Environments. Virtual Environments ’95, 135-148. doi:10.1007/978-3-7091-9433-1_12Slater, M., Usoh, M., & Steed, A. (1995). Taking steps. ACM Transactions on Computer-Human Interaction, 2(3), 201-219. doi:10.1145/210079.210084Slater, M., McCarthy, J., & Maringelli, F. (1998). The Influence of Body Movement on Subjective Presence in Virtual Environments. Human Factors: The Journal of the Human Factors and Ergonomics Society, 40(3), 469-477. doi:10.1518/001872098779591368So, R. H. Y., Lo, W. T., & Ho, A. T. K. (2001). Effects of Navigation Speed on Motion Sickness Caused by an Immersive Virtual Environment. Human Factors: The Journal of the Human Factors and Ergonomics Society, 43(3), 452-461. doi:10.1518/001872001775898223Steuer, J. (1992). Defining Virtual Reality: Dimensions Determining Telepresence. Journal of Communication, 42(4), 73-93. doi:10.1111/j.1460-2466.1992.tb00812.xSullivan, G. M., & Feinn, R. (2012). Using Effect Size—or Why the P Value Is Not Enough. 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Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools