The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - October-December (2009 vol.2)
pp: 200-211
Colin Swindells , University of Victoria, Victoria
Karon E. MacLean , University of British Columbia, Vancouver
Kellogg S. Booth , University of British Columbia, Vancouver
ABSTRACT
We examine a crucial aspect of a tool intended to support designing for feel: the ability of an objective physical-model identification method to capture perceptually relevant parameters, relative to human identification performance. The feel of manual controls, such as knobs, sliders, and buttons, becomes critical when these controls are used in certain settings. Appropriate feel enables designers to create consistent control behaviors that lead to improved usability and safety. For example, a heavy knob with stiff detents for a power plant boiler setting may afford better feedback and safer operations, whereas subtle detents in an automobile radio volume knob may afford improved ergonomics and driver attention to the road. To assess the quality of our identification method, we compared previously reported automated model captures for five real mechanical reference knobs with captures by novice and expert human participants who were asked to adjust four parameters of a rendered knob model to match the feel of each reference knob. Participants indicated their satisfaction with the matches their renderings produced. We observed similar relative inertia, friction, detent strength, and detent spacing parameterizations by human experts and our automatic estimation methods. Qualitative results provided insight on users' strategies and confidence. While experts (but not novices) were better able to ascertain an underlying model in the presence of unmodeled dynamics, the objective algorithm outperformed all humans when an appropriate physical model was used. Our studies demonstrate that automated model identification can capture knob dynamics as perceived by a human, and they also establish limits to that ability; they comprise a step towards pragmatic design guidelines for embedded physical interfaces in which methodological expedience is informed by human perceptual requirements.
INDEX TERMS
Haptic I/O, evaluation/methodology, human factors, software psychology.
CITATION
Colin Swindells, Karon E. MacLean, Kellogg S. Booth, "Designing for Feel: Contrasts between Human and Automated Parametric Capture of Knob Physics", IEEE Transactions on Haptics, vol.2, no. 4, pp. 200-211, October-December 2009, doi:10.1109/TOH.2009.23
REFERENCES
[1] I. Brouwer, “Cost-Performance Trade-Offs in Haptic Design,” MSc thesis, Dept. of Mechanical Eng., Univ. of British Columbia, 2004.
[2] S. Choi and H.Z. Tan, “Perceived Instability of Virtual Haptic Texture. I. Experimental Studies,” Presence: Teleoperators and Virtual Environments, vol. 13, no. 4, pp. 395-415, 2004.
[3] J.E. Colgate and G. Schenkel, “Passivity of a Class of Sampled-Data Systems: Application to Haptic Interfaces,” Proc. Am. Control Conf., 1994.
[4] N. Cowan, “The Magical Number 4 in Short-Term Memory: A Reconsideration of Mental Storage Capacity,” Behavioral and Brain Sciences, vol. 24, no. 1, pp. 87-114, 2001.
[5] N. Forrest, S. Baillie, and H.Z. Tan, “Haptic Stiffness Identification by Veterinarians and Novices: A Comparison,” Proc. World Haptics, pp. 1-6, 2009.
[6] P.R. Gill, W. Murray, and M.H. Wright, Practical Optimization, pp.136-137. Academic Press, 1981.
[7] Immersion Corporation, PR-1000 Rotary Controller with Braking Actuator, available online at: http://www.immersion.com/industrial/rotary products, 2006.
[8] P. Jordan, Designing Pleasurable Products: An Introduction to the New Human Factors. CRC Press, 2000.
[9] K.J. Kuchenbecker, J. Fiene, and G. Niemeyer, “Improving Contact Realism Through Event-Based Haptic Feedback,” IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 2, pp. 219-230, Mar./Apr. 2006.
[10] W.B. Knowles and T.B. Sheridan, “The ‘Feel’ of Rotary Controls: Friction and Inertia,” Human Factors, vol. 8, pp. 209-215, 1966.
[11] B.E. Miller, J.E. Colgate, and R.A. Freeman, “Guaranteed Stability of Haptic Systems with Nonlinear Virtual Environments,” IEEE Trans. Robotics and Automation, vol. 16, no. 6, pp. 712-719, Dec. 2000.
[12] K.E. Novak, L.E. Miller, and J.C. Houk, “Kinematic Properties of Rapid Hand Movements in a Knob Turning Task,” Experimental Brain Research, vol. 132, no. 4, pp. 419-433, 2000.
[13] C. Richard, “Friction Identification and Haptic Display of Friction,” Proc. Haptic Interfaces for Virtual Environments and Teleoperator Systems (HAPTICS), 1999.
[14] R.J. Snowden, “Sensitivity to Relative and Absolute Motion,” Perception, vol. 21, no. 5, pp. 563-568, 1992.
[15] S.S. Stevens, “On the Psychophysical Law,” Psychophysical Rev., vol. 64, no. 3, pp. 153-181, 1957.
[16] C. Swindells and K.E. MacLean, “Capturing the Dynamics of Mechanical Knobs,” Proc. Second Joint Eurohaptics Conf. and Symp. Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 194-199, Mar. 2007.
[17] M.T. Turvey, “Dynamic Touch,” Am. Psychologist, vol. 51, no. 11, pp. 1134-1152, 1996.
[18] B. Woodruff and H. Helson, “Torque Sensitivity As a Function of Knob Radius and Load,” Am. J. Psychology, vol. 80, no. 4, pp. 558-571, 1967.
25 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool