Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
Hong Z Tan • 1ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Hong Z. TanAssociate Professor
Electrical and Computer EngineeringMechanical Engineering (courtesy)Psychological Sciences (courtesy)
Faculty FellowEnvision Center for Data Perceptualization
Purdue [email protected]
All About Thresholds:An Overview of Haptic Perception of Mechanical Properties
Hong Z Tan • 2ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Enactive Knowledge“Enactive knowledge is neither symbolic nor iconic. It is natural and intuitive, based on
experience and the perceptual consequences of motor acts. Enactive knowledge is information gained through perception-action interactions with the environment.”
Hong Z Tan • 3ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Motivation
My own research focuses on haptic perceptionAny effective enactive interface should capitalize on our exquisite sensitivity to object properties through physical interactions with the environmentUnderstanding our sensitivity to mechanical properties can lead to enactive interfaces that match the human sensory-motor capabilitiesIn this context, haptic perception is an integral component of enactive knowledge
Hong Z Tan • 4ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Outline
Overview of human discrimination thresholds for mechanical properties of objectsAn example of using thresholds for interface engineeringPredicting perception of complex signals from the perceptual thresholds for the components
Hong Z Tan • 5ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
When you use a force display…
F0 (force)magnitudedirection
K (stiffness / compliance)x (displacement / length)
linearangular (α, joint-angle position)
B (viscosity)
L&&& ++++= xMxBKxFF 0
Discrimination threshold The smallest difference between two stimuli that can be reliably detected;
Just Noticeable Difference(JND).
Hong Z Tan • 6ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Force Magnitude Perception
Pang, Tan, & Durlach (1991). Manual discrimination of force using active finger motion. Perception & Psychophysics, 49(6), 531-540.
L&&& ++++= xMxBKxFF 0
Hong Z Tan • 7ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Force JND with Fixed Displacement
Problem:Confounding
Mechanical workcues! Average JND: 8%
Hong Z Tan • 8ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Force JND with Roving Displacement
Randomizedmechanical work cues
Average JND:7% (fixed displacement)
14% (roving displacement)
Hong Z Tan • 9ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Related Work by Lynette Jones
Photo courtesy of Lynette Jones
Contra-lateral limb matching of force; JND = 7%(Jones, 1989)
Jones (1989). Matching forces: Constant errors and differential thresholds. Perception, 18, 681-687.
Hong Z Tan • 10ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Force Direction Perception
Barbagli, Salisbury, Ho, Spence, & Tan (2006). Haptic discrimination of force direction and the influence of visual information. ACM Transactions on Applied Perception, 3(2), 125-135.
Force-direction JND:H only: 25.6°
H with congruent V: 18.4°H with incongruent V: 31.9°
L&&& ++++= xMxBKxFF 0
Hong Z Tan • 11ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Force-direction JND is Independent of Reference F Direction
Tan, Barbagli, Salisbury, Ho, & Spence (2006). Force-direction discrimination is not influenced by reference force direction (Short Paper). Haptics-e, 4(1).
Hong Z Tan • 12ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Stiffness/Compliance Perception
Tan, Durlach, Beauregard, & Srinivasan (1995). Manual discrimination of compliance using active pinch grasp: The roles of force and work cues. Perception & Psychophysics, 57(4), 495–510.
Jones & Hunter (1990). A perceptual analysis of stiffness. Experimental Brain Research, 79, 150-156.
Jones & Hunter (1990)
Contra-lateral limb matching of stiffnessStiffness JND = 23%
L&&& ++++= xMxBxKFF 0
Hong Z Tan • 13ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Average Compliance JNDs(1,536 trials per point)
Preloaded spring;fixed displacement;
confounding force cues;no more mechanical work cues
Compliance JND:15% − 99%
Hong Z Tan • 14ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Terminal Force Hypothesis
⎟⎟⎠
⎞⎜⎜⎝
⎛Δ+
Δ=Δ
ΔΔ
00
00
00
0
0
/)(1/)(
2)(
JND force equivalent )(JNDcompliance )(
CCCC
CDF
FC
Compliance JND:15% − 99%
Terminal-Force JND:5.15 % (s.d.=0.76%)
Hong Z Tan • 15ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Length Perception
Durlach, Delhorne, Wong, Ko, Rabinowitz, & Hollerbach (1989). Manual discrimination and identification of length by the finger-span method. Perception & Psychophysics, 46(1), 29–38.
L&&& ++++= xMxBxKFF 0
Hong Z Tan • 16ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
]598.0log250.0)[3.4/(L)( 000 +−=Δ LLJND
JND=1mm@ 10mm ref
(10%)
JND=2.4mm@ 80mm ref
(3%)
Hong Z Tan • 17ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Finger Joint-Angle Perception (α)
Tan, Srinivasan, Reed, & Durlach (2007). Discrimination and identification of finger joint-angle positions using active motion. ACM Transactions on Applied Perception, 4(2), Article 10, 14 pp.
PIP joint JND: 2.5°, regardless of PIP or MCP joint positionMCP joint JND: 2.0°−2.7°, better when PIP/MCP is straight
Derived JND at fingertip: ≈ 2.2 mm
L&&& ++++= xMxBxKFF 0
Hong Z Tan • 18ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Perception of Viscosity
Contralateral limb-matching procedure
L&&& ++++= xMxBKxFF 0
Jones & Hunter (1993). A perceptual analysis of viscosity. Experimental Brain Research, 94, 343-351.
ViscosityJND = 34%
Hong Z Tan • 19ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Summary of JNDs
VariableForce
Magnitude ………..Direction ………....
Stiffness ………………Displacement
Linear ………….....Angular …………...
Fingertip …......Viscosity ……………....
JND
5−10 %25−35°22 %
3−10 %2.0−2.7°2.2 mm34%
L&&& ++++= xMxBKxFF 0
Hong Z Tan • 20ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Outline
Overview of human discrimination thresholds for mechanical properties of objectsAn example of using thresholds for interface engineeringA study on perception of complex waveforms, using threshold measurements
Hong Z Tan • 21ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Haptic Data Compression using Force JND
Hinterseer & Steinbach (2006). A psychophysically motivated compression approach for 3D haptic data. Proceedings of Haptics Symposium ‘06, 35-41. Slide courtesy of Peter Hinterseer
t
Do nothing Transmit new value
1-D
2-D
Fprevious
Fnew
deadzone deadzone
Fprevious
Fnew
Hong Z Tan • 22ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
(cont.)
3-D spherical deadzoneUser testing showed that packet rate can be reduced by up to 90% for the transmission of force magnitude
Further reduction in packet rate may be achieved by incorporating the discrimination thresholds for force direction
Hong Z Tan • 23ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Outline
Overview of human discrimination thresholds for mechanical properties of objectsAn example of using thresholds for interface engineeringA study on perception of complex waveforms, using threshold measurements
Hong Z Tan • 24ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Perception of Texture Gratings
Mini-stick3-Degrees of FreedomNominal 1μm resolutionForce commands updated at 2kHz
Question:Can the perception of complex texture gratings be predicted from that of simple texture gratings?
Cholewiak & Tan (submitted). A Frequency Analysis of the Detectability and Discriminability of Virtual Texture Gratings. Submitted to ACM TAP.
Hong Z Tan • 25ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Two Types of Texture Gratings
Sinusoidal grating Square-wave grating
)2sin()(sin λπxAxh =⎩⎨⎧
≤−>
=0)2sin(,0)2sin(,
)(λπλπ
xifAxifA
xhsquare
x
y
z x
y
z
Hong Z Tan • 26ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Components of a Square-Wave
Fundamental Frequency Component A*4/π* sin(2πx/λ)
3rd Harmonic Component A*4/(3π)* sin(3*2πx/λ)
5th Harmonic Component A*4/(5π)* sin(5*2πx/λ)
A
Hong Z Tan • 27ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Components of a Square-Wave
Hong Z Tan • 28ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Components of a Square-Wave
A*4/π
A
> A
Hong Z Tan • 29ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Sinusoidal Threshold Results
Hong Z Tan • 30ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Square-Wave Threshold Results
Hong Z Tan • 31ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Ratio Results
Hong Z Tan • 32ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
But …
Our results means that when the amplitudes are very small (i.e., near detection threshold), a square-wave grating should feel like a sinusoidal grating!Indeed, we were able to show that at low amplitudes, a square-wave grating was indistinguishable from a sinusoidal grating untilthe amplitude of its 3rd harmonic component exceeded detection threshold
Hong Z Tan • 33ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Concluding Remarks
Detection/discrimination thresholds go a long way towards our understanding of human perception of mechanical properties of real or virtual objectsThresholds for simple signals can be used to predict those for complex signalsThey are useful for determining the resolution of human-computer interfaces, for designing data compression algorithms, etc.Thresholds can be useful for the design of enactive interfaces!
Hong Z Tan • 34ENACTIVE ’07 Conference, Grenoble, France, November 20, 2007
Acknowledgment
CollaboratorsFederico BarbagliG. Lee BeauregardSteven A. CholewiakNat DurlachCristy HoXiao-Dong PangCharlotte ReedKen SalisburyCharles SpenceMandayam Srinivasan
Funding AgenciesOffice of Naval ResearchNational Institutes of HealthFairchild FoundationMIT G.Y. Chu FellowshipNaval Air Warfare Center Training Systems DivisionOxford McDonnell-Pew Centre for Cognitive NeuroscienceFord Motor CompanyNational Science Foundation