Download Electrostatic Force on a Human Fingertip

NORTHWESTERN UNIVERSITY
Electrostatic Force on a Human Fingertip
A DISSERTATION
SUBMITTED TO THE GRADUATE SCHOOL
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
for the degree
MASTER OF SCIENCE
Field of Mechanical Engineering
By
David John Meyer
EVANSTON, ILLINOIS
December 2012
2
c Copyright by David John Meyer 2012
All Rights Reserved
3
ABSTRACT
Electrostatic Force on a Human Fingertip
David John Meyer
The human fingertip is extremely sensitive to lateral (shear) forces that arise in surface
exploration. Several designers developed haptic displays that modulate surface friction to generate programmable tactile sensations on surfaces. Electrostatic attraction has the demonstrated
ability to pull a human fingertip into greater contact with a surface and increase the surface
friction. Although commercial development of electrostatic tactile displays has begun, a rigorous
understanding of the fingertip-surface interface is lacking.
In this thesis, I discuss the underlying physics of electrostatic attraction, and derive a mathematical model for electrostatic force on a human fingertip. I have explored several methods
for quantifying the force experimentally, and have developed a tribometer to measure lateral
friction forces on a fingertip under well-controlled conditions. Although I observe a large person
to person variability, I show an expected square law relationship between actuation voltage and
friction force (and inferred electrostatic force). I modeled a dependence of the electrostatic force
on actuation frequency, but observe results that are distinctly inconsistent with my model.
4
Acknowledgements
I’d like to thank Ed Colgate and Michael Peshkin for their encouragement and guidance.
Their commitment to scientific rigor has given me a new appreciation for research, and I cannot
thank them enough for always expecting the best and advising me on how to get there.
Thanks go to Paul Barnes for his work with fingerprint imaging, and Michael Wiertlewski
for his helpful discussions and eye for graphical representation. Thanks to the rest of the
Surface Haptics group for those endless brainstorming meetings every week, and LIMS for those
occasional two-hour lunch breaks.
I thank my parents, Robert Meyer and Susan Conry, for teaching me to strive for perfection
but to be happy with a job well done. I would not have made it this far without their unwavering
support.
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Table of Contents
ABSTRACT
3
Acknowledgements
4
List of Tables
7
List of Figures
8
Chapter 1.
Introduction
10
Chapter 2.
Background
12
2.1.
Friction Modulation in Surface Haptic Devices
12
2.2.
Electrostatic Force used for Tactile Feedback
13
Chapter 3.
Generating Electrostatic Force
15
3.1.
Electrostatic Theory
16
3.2.
Dynamic Analysis
20
Chapter 4.
Measuring Electrostatic Force
24
4.1.
Measurement Considerations
25
4.2.
Tribometer Construction
28
4.3.
Electrostatic Friction
30
4.4.
Friction Analysis
32
Chapter 5.
Analyzing Force and Electronics
36
6
5.1.
Inferred Electrostatic Force Data
37
5.2.
Fingertip Electric Characterization
38
Chapter 6.
6.1.
Conclusion
Future Work
References
41
41
43
Appendix A.
Dynamic Analysis and Simulation of Touch Screen Circuit
46
Appendix B.
Tribometer Control System Design and Electronics
48
B.1.
PC104 Stack Control System
49
B.2.
Motor Amplifiers
51
B.3.
RS232 Function Generator
52
7
List of Tables
B.1
Connections to the PC104 stack
49
B.2
Function generator amplitude correction settings (All values are voltages)
53
8
List of Figures
3.1
One dimensional schematic of the screen-finger interface.
16
3.2
Circuit element manifestation of finger and screen
21
3.3
Circuit diagram of the system
21
3.4
Predicted Force over Frequency
23
4.1
Force diagram of finger-screen interface
25
4.2
Tribometer drawing
28
4.3
Block diagram of experimental setup
29
4.4
Overview of raw friction data taken with tribometer
30
4.5
Electrostatic friction under low-frequency AC actuation
31
4.6
Overview of raw data taken for 10 kHz
32
4.7
Raw data with averaged data points shown
33
4.8
Sample lateral versus normal force plot of averaged data
34
5.1
Electrostatic force results
37
5.2
Finger impedance measurement experimental setup
39
5.3
Finger impedance data
40
A.1
Circuit diagram of the system
46
B.1
Tribometer control system schematic
48
9
B.2
Graphical control interface for experiments
50
B.3
Linear motor amplifier schematic
51
B.4
Linear motor amplifier
52
B.5
Function generator amplitude correction schematic
53
10
CHAPTER 1
Introduction
One of the most important elements of a machine is its user interface. Human interfaces
define the practical utility of a multitude of devices, from radios and thermostats to cars and
industrial equipment. A simple device with a simple interface is oftentimes of greater use than
a powerful device that is hard to use. For example, an unfamiliar home entertainment system
could quite possibly be useless to a visitor, whereas anyone can use an analog tuned television.
In every case, though, an interface consists of a number of sensors whose inputs are combined
into a control scheme, and feedback methods to alert the user of the state of the system. In a
well designed interface, a user easily controls the sensor inputs and quickly observes the affect
she has had on the device state.
The simplest interfaces consist of switches or buttons, and in the case of room lighting, they
fit the bill perfectly. A user presses the button and receives nearly instant feedback as the room
illuminates. If more functionality is desired, a knob or slider can add a degree of freedom, to
control the brightness of the light for example. However, in systems that are more complicated
than one light in one room, multiple knobs and buttons are needed, and are often combined into
a panel. As the complexity of the device increases, however, so does its state. Consequently, to
provide useful feedback to the user, visual displays are often used in conjunction with the panel,
as with a car radio. As we can see, there is a disturbing trend in this style of design. Plainly
stated, the more things we desire to control, the larger and more intricate the user interface
becomes. Fortunately, the recent proliferation of touch screens has provided engineers with a
11
whole slate of potential designs that are not possible with traditional knob and button style
panels.
A basic touch screen consists of a visual display beneath a sensor that detects when and
where a finger is touching the screen. Combining the display and sensor into one system allows for a dynamically reconfigurable virtual panel. This type of interaction allows for control
of vastly complex systems, such as those in smartphones, for example. Unfortunately, touch
screens in use today on many commercial devices only allow for rich feedback via the visual
and perhaps auditory pathways. In some cases, vibration is used to provide tactile feedback,
but only insomuch as an affirmation of touch, or other such binary signal. The haptic aspect,
the feel of buttons and knobs, is completely lost in a touch screen application. The lack of this
feedback path requires users to look at the screen during use, and can increase the difficulty of
completing a control task.
The study of surface haptics focuses on tactile interactions between a fingertip and a physical
surface. Tactile perception at the finger is almost exclusively a result of mechanical forces applied
to the fingertip in contact with the surface. Lateral (shear) force has been shown to create a
powerful percept in human haptic exploration[1]. One approach to surface haptics is to control
the lateral force on a fingertip in contact with a touch screen. Surface friction is the basis for all
lateral interaction forces between a finger and surface. Thus, a device must predictably modulate
surface friction to use lateral force to create haptic effects. Since friction is the basis for all later
interaction forces between a finger and a flat surface, any device that uses lateral forces to render
a haptic environment must successfully and predictably modulate surface friction.
12
CHAPTER 2
Background
2.1. Friction Modulation in Surface Haptic Devices
Several researchers have been successful in modulating surface friction through mechanical
actuation. Nara et al[2] used surface acoustic waves to reduce the effective friction of a linear
slider. The slider, which rested on top of steel balls on a substrate, exhibited less friction when
standing surface acoustic waves were applied to the substrate. Virtual textures were generated
to be felt by a finger resting on the slider. Focusing on the bare finger, Watanabe and Fukui[3]
built a device which created a sensation of “smoothness” when ultrasonic vibration was applied
to the surface. This smoothness was attributed to a thin film of air, or “squeeze film,” that
develops between the surface and fingertip. Northwestern’s Tactile Pattern Display (TPaD)
[4, 5] combines finger position sensing with ultrasonic vibration to create customizable textures
on a glass surface.
Three additional improvements have been made since the TPaD at Northwestern, all of
which implement active lateral forcing on the fingertip. The ShiverPaD[6] vibrated a TPaD
laterally in one dimension at roughly 800 Hz and pulsed on the ultrasonic friction reduction for
only a portion of each lateral motion cycle. By changing which portion of the full cycle was
low friction, the device produced a lateral force on the finger. LateralPaD[7], developed a few
years later, implemented a similar idea to ShiverPaD, but increased the frequency of the lateral
motion to an ultrasonic range to reduce excess noise. ActivePaD[8] implements lateral velocity
control with large amplitude motion. The ActivePaD consisted of a TPaD mounted on a 3 DOF
13
planar actuator over a stationary visual display. Combining friction modulation with lateral
motion, the device rendered complex haptic environments.
Mechanical actuation, especially at ultrasonic frequencies, requires a significant amount of
energy. While system resonances can be exploited to reduce energy loss, this technique limits the
design flexibility of such devices. With the ever-increasing presence of touch interfaces on mobile
devices, purely electronic tactile displays show promise of practical application. Electronic tactile
displays fall into two categories. Electrocutaneous displays stimulate tactile receptors in the
fingertip by passing small amounts of electric current though the skin. Electrostatic displays use
electrostatic attraction forces to increase the friction force on a finger sliding across the surface.
Recent contributions from Bau et al. (TeslaTouch)[9] and Linjama et al. (E-Sense)[10] have
used a method termed “electrovibration,” to create a compelling sense of texture on a surface.
Electrovibration uses AC waveforms to pulse electrostatic attraction on and off, resulting in
vibratory feeling in a finger traveling across the surface. These two developments have shown
the viability of electrostatic displays for low-power versatile applications.
2.2. Electrostatic Force used for Tactile Feedback
Mallinckrodt et al.[11] first observed “electrically induced vibrations” in 1953 while touching
a high-voltage (110V) AC conductor coated with a thin layer of insulator. This effect was
determined[12] to be a mechanical interaction between the surface and fingertip induced by an
intermittent electrostatic attractive force between the skin and conductor. Electrostatic force
was first used in haptics when Strong and Troxel[13] developed an electrode-array display, which
used friction enhanced by electrostatic attraction to generate texture sensations on a surface. In
their study, they observed that the intensity of the vibration sensation was primarily due to the
peak applied voltage. Beebe et al.[14] developed a polyimide-on-silicon tactile display that used
electrostatic force to create 100 Hz pulses of high friction to render textures or vibration on a
14
surface. In a following study, Tang and Beebe[15] showcased the haptic ability of this display
with tests by visually impaired subjects of detection threshold, line separation, and pattern
recognition.
Researchers have since studied the electrostatic effect further by observing how human detection threshold depends on various conditions. Agarwal et al.[16] observed the relationship
between dielectric thickness and voltage at the detection threshold, and Kaczmarek et al.[17]
investigated the differences in detection for positive and negative voltages. While these efforts
have studied human response to electrical stimulations, none have measured the mechanical
effect of electrostatic attraction directly, and a detailed understanding of the finger-surface interface is still lacking. The focus of my work is to improve our knowledge of electrostatic force
on a fingertip and its effect on finger-surface interaction.
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CHAPTER 3
Generating Electrostatic Force
The Lorentz Force law states that a charged particle in an electric field feels a force proportional to the strength of the field. This fundamental relationship is the underlying principle
behind electrostatic haptic displays; an electrically grounded finger is in contact with a conductive surface coated with a thin insulating layer. When the conductive surface is charged,
an electric field arises in the insulator and fingertip tissue. In this chapter, I examine the electrical physics and dynamics in this finger-surface interface to create a predictive model for the
electrostatic force on a fingertip.
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3.1. Electrostatic Theory
The tactile display used in my research is a 3M MicroTouch screen, chosen because of its
availability and construction. The screen consists of two thin layers deposited across the entire
6.7 inch diagonal glass substrate. The first layer on top of the glass is a thin layer of indium
tin oxide (ITO), a transparent conductor roughly 40 nanometers thick. Atop the ITO sits a
slightly textured layer of silica about one micron thick. The silica, an insulator, is the outermost
layer and is in contact with the fingertip. Although this device was designed as a finger position
sensor, these layers can also be used to generate electrostatic force on a fingertip. A schematic
highlighting the relevant layers and electrical properties is shown in figure 3.1.
Conductive Tissue
Stratum Corneum (εsc , dsc , Esc , Csc )
VF
Air Gap (dg , Eg )
Fingertip
Qf
Insulator (SiO2 ) (εi , di , Ei , Ci )
ITO
3M MicroTouch
Glass
Figure 3.1. One dimensional schematic of the screen-finger interface.
The fingertip consists of conductive tissue beneath the outer layer of skin, called the stratum
corneum. At about 200 microns thick, and consisting mostly of dead skin cells, the stratum
corneum acts primarily as an insulating dielectric, but may have a finite resistivity. When a
voltage, VF , is applied, the capacitor formed by the finger-screen contact is charged up. If there
is a leakage current through the stratum corneum, free charge, Qf , will build up on the surface
of the screen, in the middle of the capacitor. Using the principle of virtual work, the force on
the conducting tissue of the fingertip is derived for this parallel plate capacitor model.
17
In the diagram, ε are the relative permitivities, d are the layer thicknesses, E are the electric
fields, and C are the capacitances of each of the layers. Assuming fringe fields are small enough
to neglect, the electrostatic equations as given by Gauss’s Law and the definition of electric
potential are:
−VF = di Ei + dg Eg + dsc Esc
(3.1)
Eg = εsc Esc
εi Ei = Eg +
Qf
Aε0
where A is the contact area of the fingertip, and ε0 is the permeability of free space. Solving for
the electric fields in each layer yields the expressions in equation 3.2 and the potential energy
given in equation 3.3.
(3.2)
(3.3)
Esc =
−Qf di − VF Aε0 εi
Ae0 (dsc εi + di εsc + dg εi εsc )
Eg =
−Qf di εsc − VF Aε0 εi εsc
Ae0 (dsc εi + di εsc + dg εi εsc )
Ei =
Qf (dg εsc + dsc ) + VF Aε0 εsc
Ae0 (dsc εi + di εsc + dg εi εsc )
1
2
U = Aε0 εsc dsc Esc
+ dg Eg2 + εi di Ei2
2
The electrostatic force on the fingertip is the derivative of the potential energy with respect to
the air gap. When the finger is in contact with the screen, this expression is evaluated as dg
approaches zero, given in equation 3.4.
(3.4)
ε2sc (−Qf di + VF Aε0 εi )(Qf di + VF Aε0 εi )
∂U =
Fe = −
∂dg dg →0
2Aε0 (dsc εi + di εsc )2
18
Defining capacitances for the stratum corneum and insulator as shown in 3.5, it is much easier
to understand the terms, shown in 3.6.
(3.5)
Ci =
ε0 εi A
di
Csc =
2
Ci2 Csc
(3.6)
Fe =
VF2 −
ε0 εsc A
dsc
Q2f
Ci2
2Aε0 (Ci + Csc )2
By inspection, this equation reveals the square law dependence of electrostatic force on
voltage, as well as the dependence on capacitances of the stratum corneum and insulating layer
atop the ITO. More interestingly, in the case that the free charge on the surface of the glass
builds up to match that on the ITO (Qf → Ci V ), the electrostatic force on the finger completely
disappears. This is because the charge on the surface of glass reduces the electric field in the
fingertip. Charge can flow to the surface through any resistive path, which in this case can be
the fingertip itself.
Assuming that no charge flows to the surface of the glass, however, the electrostatic force
on the finger is the same as the force on a capacitor with two dielectrics in series. In terms of
only non-derived quantities, this force is:
(3.7)
ε0 AVF2
Fe =
2
di dsc
+
εi
εsc
2
Equation 3.7 agrees with previously published work by Strong and Troxel[13]. However, this
has been more recently contested by Kaczmarek et al. [17], who noticed that if the insulating
19
layer is zero thickness, the model predicts:
(3.8)
F1 =
ε0 AVF2
2
dsc
2
εsc
which does not agree with the standard textbook definition of force on a parallel plate capacitor,
given by:
(3.9)
F2 =
ε AV 2
0 F
dsc
dsc
2
εsc
The difference between the two models, F1 and F2 , is a multiplication factor of relative permittivity of the dielectric in the capacitor. I argue that equation 3.9 is an incorrect application of
electrostatic theory to the fingertip-surface system, and that F2 is not the proper expression for
force on the fingertip.
This discrepancy can be more easily analyzed with the Lorentz force law, relating force on a
sheet of charge to the electric field. Assuming zero insulator thickness in both models, Q = CVF ,
and C =
(3.10)
(3.11)
ε0 εsc A
dsc ,
equations 3.8 and 3.9 become:
F1 =
F2 =
Q2
2ε0 A
Q2
2ε0 εsc A
Let us consider the two conducting surfaces as sheets of constant charge. The electric field in a
vacuum near a large sheet of charge (the ITO) is derived using Gauss’ law as E =
Q
2ε0 A .
The
force on a parallel sheet of equal charge (the conductive finger tissue) is F = QE, which agrees
with F1 . If we then assume the world is filled with a dielectric fluid, the electric field is reduced
by a factor of the relative permittivity. Thus, the force on a sheet of charge inside the dielectric
20
also reduced, as predicted by the model F2 . This is only true, though, if the sheets of charge
are inside the dielectric and experience the reduced electric field.
In the case where a slab of dielectric is introduced between two sheets of constant charge, I
argue that the force between the two sheets does not change, because the electric field is reduced
only inside the dielectric. The slab instead reduces the voltage difference between the sheets.
The model F2 is only correct for a world filled with a liquid dielectric, in which all electric
field strengths are reduced by a factor of the relative permittivity. F1 is the proper model for
a capacitor with a slab of solid dielectric between two conductors, which is the case for the
stratum corneum and insulator on the touch screen. Therefore, the electrostatic force on the
fingertip is correctly described by equation 3.6.
3.2. Dynamic Analysis
Electrostatic theory predicts that the force on the fingertip is determined by the capacitive
capabilities of the skin-surface interface, as well as the free charge on the surface of the glass. It
is therefore crucial to understand the dynamics of the entire electrical system in order to create a
known electrostatic force on the finger. A dynamic analysis also explains how a finite resistivity
in the stratum corneum allows charge to flow to the surface and reduce the force on the finger.
To begin the analysis, I developed a simple linear circuit model for the finger and 3M screen.
In the figure 3.3, Csc and Ci are the capacitances of the stratum corneum and insulator
respectively as defined in section 3.1. Additional elements which don’t directly contribute to
electrostatic force on the finger include the series resistance in the finger, Rf , and finite resistance
of the stratum corneum, Rsc . A series capacitance, Cs , is also modeled because the touch screen
has a capacitor in the cable between the input and ITO.
Referring to equation 3.6, it is clear that the only two dynamic elements which affect the
electrostatic force on the fingertip are VF , the voltage drop from the ITO layer to the finger
21
Copper Foil
VA
1µm SiO2
Indium Tin Oxide
Glass
Figure 3.2. Circuit element manifestation of finger and screen
Rf
Csc
Qf
Rsc
VA
VF
Ci
Cs
Figure 3.3. Circuit diagram of the system
tissue, and Qf , the charge which leaks through the stratum corneum and accumulates on the
surface. A dynamic analysis of this developed linear model shown above predicts the electrostatic
force on the finger for any given actuation signal, VA , and reveal frequencies at which a force
will be produced.
Using Kirchhoff’s current and voltage laws, two transfer functions describing the behavior
of Qf and VF with respect to the supply voltage are written. These transfer functions, derived
22
in Appendix A, are shown in equation 3.12.
h
i
Cs + Csc Rsc (Ci + Cs ) s
(3.12)
VF (s)
i h
i
h
i
=h
VA (s)
Ci + Cs + Csc Rsc (Ci + Cs ) + Ci Cs (Rf + Rsc ) s + Ci Cs Csc Rf Rsc s2
Qf (s)
Ci Cs
i h
i
h
i
=h
VA (s)
Ci + Cs + Csc Rsc (Ci + Cs ) + Ci Cs (Rf + Rsc ) s + Ci Cs Csc Rf Rsc s2
To then calculate the force produced by a certain supply voltage signal, the equations in 3.12
are simulated in the time domain using MATLAB. Plugging the resulting time-varying signals
VF and Qf into equation 3.6 results in a time-varying electrostatic force. Averaging this force
over time gives the electrostatic force felt by the fingertip.
To establish a starting point for experimental work, I completed this analysis using estimated
circuit parameters. Using skin properties previously published[18], the resistivity and dielectric
constant of the stratum corneum at 1 kHz are ρsc ≈ 33 kΩm and εsc ≈ 1000 respectively.
Assuming the contact area of the finger is a circle with radius 4mm, the electrical parameters
are:
Rsc =
(3.13)
Csc =
ρsc dsc
(33kΩm)(200µm)
=
= 130kΩ
A
π(4mm)2
F
(8.85 × 10−12 m
)(1000π)(4mm)2
ε0 εsc A
=
= 2.2nF
dsc
200µm
Ci =
F
(8.85 × 10−12 m
)(3.9π)(4mm)2
ε0 εi A
=
= 1.7nF
di
1µm
The estimated series resistance is 500 Ω, the capacitor in the screen is 140 pF , and the
resulting predicted electrostatic force for 140 V actuation signals of varying frequencies is shown
in figure 3.4. At low frequencies, the resistivity of the stratum corneum allows charge to flow
from the body and effectively nullify the force on the fingertip. At high frequencies, charge
cannot build up on the capacitors in the circuit due to the finger resistance, Rf . Because the
23
Electrostatic Force
0.2
0.15
0.1
0.05
0
101
102
103
104
105
106
Frequency (Hz)
107
108
109
Figure 3.4. Predicted Force over Frequency
electrical properties of fingertips are highly variant, the values of these knee frequencies are quite
uncertain. Nevertheless, the model predicts that DC voltages create no electrostatic force, and
suggests that force is produced at a wide range of frequencies.
24
CHAPTER 4
Measuring Electrostatic Force
The goal of my experimental work is to verify the models presented in the previous chapter. In an ideal world, electrostatic attraction would be measured under different actuation
signals and compared with theory. Unfortunately, measuring the electrostatic attraction is very
problematic. Typically, a force is measured with a transducer placed in the load path, but this
method is not possible at the finger-screen interface. The electrostatic force relies on an electric
field being present at the fingertip, and any force transducer between the finger and glass would
disrupt the electric field substantially. For this reason, I measured the friction modulation due
to electrostatic attraction, referred to as electrostatic friction.
25
4.1. Measurement Considerations
Before I started measuring friction, I considered two other methods to directly measure
electrostatic attraction. The first method involved measuring energy in the capacitive coupling
formed by the finger and screen. The second focused on imaging optically the compression of
the fingertip under electrostatic force. Figure 4.1 is a simplified diagram showing forces and
stiffnesses in the finger-screen interface. Spring k1 represents the soft tissue between the bone
Finger
Electrostatic Force
Pressing Force
k1
k3
k2
3M screen
Soft Tissue
Stratum Corneum
Figure 4.1. Force diagram of finger-screen interface
and fingertip surface, k2 models the stiffness of the stratum corneum, and k3 is a load cell. There
is an important distinction to be made between the “pressing force” and “electrostatic force”.
Both are normal forces at the fingertip-screen contact, but the former is applied through the
bone, whereas the latter is only present in the shallow fingertip tissue, represented by sping k2 .
The relative stiffnesses of these springs provide useful insight to the behavior of the fingertip
under electrostatic force. The elastic modulus of the stratum corneum has been previously
recorded around 107 − 108 Pa[19]. Calculating k =
EA
t
for a finger in contact with the screen,
26
the stiffness of the stratum corneum is around k2 ≈ 107 N/m. Compared with the stiffness of
the soft tissue, recorded previously around k1 ≈ 103 N/m[20], the stratum corneum is effectively
incompressible. Because all the springs are in series, each one is loaded by the pressing force.
The stratum corneum experiences the addition of pressing force and electrostatic force, as is
shown in the diagram. Because k2 is relatively incompressible, an electrostatic force will not
cause much deflection, and will not be measurable by the load cell, k3 . I instead attempted an
energy-based approach to measure electrostatic attraction.
The energy in a capacitor is closely related to the force on the two capacitor plates. In
the case of the finger-screen interface there is potential energy in the capacitive bond when an
electrostatic force is present (equation 3.3). One way to measure this energy is to push the
finger into the glass, turn the actuation voltage on, then pull the finger away from the glass.
The energy used to push the finger into the glass is stored in the three springs shown in figure
4.1. With the electrostatic force applied, however, the energy recovered by pulling the finger
away from the glass is reduced. The difference in the energy during the push in phase and pull
away phase is the energy in the electrostatic bond.
Unfortunately, the energy-based approach also suffers from the softness of the finger tissue.
Based on data from my preliminary experiments, pushing the finger into the screen with 0.5 N
of pressing force uses about 150 µJ of energy. Electrostatic theory derived in section 3.1 predicts
the energy in the capacitive bond at 0.2 µJ. The force sensor cannot reliably distinguish this
small of an energy difference. Because I couldn’t successfully measure the electrostatic attraction
with a separate force transducer, I considered the fingerprint itself a transducer, and explored
the possibility of optically assessing fingerprint compression.
A finger in contact with a flat surface rests on the epidermal ridges of the fingerprint. The
real contact area encapsulates only the tips of the ridges that are in direct contact with the
glass. The apparent contact area is roughly an ellipse which encloses the region of real contact.
27
As the finger is pressed into the surface with greater force, the fingertip flattens out and the
contact area increases. An electrostatic attraction on the stratum corneum should increase the
real contact area of the finger by compressing the dermal ridges. By observing this change in
real contact area, the electrostatic force can be measured optically.
Fingerprints can be imaged through a prism using the principle of frustrated total internal
reflection (FTIR) in a method similar to that of André et al[21] and Soneda and Nakana[22].
These studies showed that not only was real contact area measurable with FTIR, but that it
changes dramatically with respect to pressing force. An undergraduate student in the lab, Paul
Barnes, conducted preliminary experiments to assess whether electrostatic force would also cause
a change in contact area, but the results showed no conclusive evidence of that effect. These
results are not completely shocking though, since the pressing force is different in nature from
the electrostatic force. When the bone presses on the fingertip into the screen, a large amount
of tissue distributes the pressing force over the whole area of the finger. Electrostatic force, on
the other hand, only substantially acts on tissue that is within 100 microns of the surface, and
can only compress the epidermal ridges themselves. Since the electrostatic force acts on such a
stiff material, deflection should be very minimal, and therefore difficult to see optically.
FTIR may still be a valid method for observing fingertip compression due to electrostatic
attraction. Improving optics and image processing could yield much finer resolution than was
obtained in preliminary experiments. The work presented here, though, focuses on measuring
the effect of electrostatics on fingertip friction. Electrostatic friction is the change in friction
due to the electric field, and can be both measured and perceived by touch over a wide range of
conditions.
28
4.2. Tribometer Construction
I designed and built a tribometer, shown in figure 4.2 to measure fingertip friction force
under different conditions. Fingertip friction in general is not very constant in time, largely due
to moisture variations, and can vary significantly in just a few seconds[23]. To extract useful
information from otherwise noisy friction data, electrostatic actuation is pulsed on and off while
the finger is traversing the glass. Consequently, to capture rapid changes in electrostatic friction,
a high-bandwidth sensor is needed. Estimations from chapter 3 show the electrostatic force will
be on the order of 100 mN, so a very high-sensitivity transducer is needed to accurately measure
friction changes due to electrostatic attraction. The ATI Nano17 force/torque sensor chosen for
cable-driven
slider
controlled
DC motor
3M MicroTouch
screen
6-axis
force sensor
counterweight and
magnetic damper
Figure 4.2. Tribometer drawing
this work offers 3 mN resolution and has a resonant frequency of 7200 Hz. The sensor itself is
mounted just below the 3M screen on a balance beam which loads the finger with a pressing force
controllable by counterweights. To prevent vibratory motion, the balance beam is magnetically
damped.
The finger is held by Velcro straps in a sliding carriage, which is driven translationally by
a motor and capstan drive to keep finger velocity constant over a large portion of the screen.
To control the motor’s velocity, a shaft encoder is used in a velocity feedback loop. The control
29
system is implemented on a PC104 stack running Matlab’s xPC real-time operating system. I
designed and built an RS232-controlled function generator to create the waveforms for electrostatic actuation. Its output feeds into a high-voltage piezo amplifier to create the high voltages
necessary for electrostatic attraction. The xPC machine records all experimental data at 1kHz
and transmits it via TCP/IP to a PC running Matlab.
Function
Generator
Piezo Amp
PC104 (xPC)
Sensoray 526
RS232
TCP/IP
PC
(MATLAB)
F/T
Controller
Motor Amps
Motors
Encoders
Touch Screen
F/T
Sensor
Tribometer
Figure 4.3. Block diagram of experimental setup
A friction measurement begins by raising the screen up to the finger, and swiping the finger
back and forth across the screen with a trapezoidal velocity pattern. Beginning on the left side
of the screen, the finger is dragged back and forth several times. During each even-numbered
swipe, the electrostatic effect is switched on when the finger is on the right-hand side of the
30
glass; during an odd-numbered swipe, the left-hand side is the active portion. Data observed
when the electrostatic actuation is off is used to establish baseline friction.
Each subject washed his or her hands prior to beginning the experiment, and talcum powder
was used to dry the fingertip and eliminate friction effects due to moisture. Every forty five
seconds, the finger was removed from the surface for twenty seconds to prevent build up of
moisture due to occlusion of the sweat glands.
4.3. Electrostatic Friction
To observe electrostatic friction due to DC actuation, a finger was swiped across the screen
while a 140 volt signal used for the electrostatic actuation was switched on and off during travel.
The results of this experiment are shown in figure 4.4. The gray portions of the plot indicate
times during which the actuation voltage is applied. As is clearly shown by the dark blue line,
a DC actuation voltage does not create a measurable change in friction. Two example swipes of
the finger are shown, as indicated by the finger velocity profile in green. The first swipe shown
is even-numbered, during which the electrostatic actuation is on in the right half of the screen.
The odd-numbered swipe shows the opposite behavior, with the actuation on in the left half.
Normal force, shown in light blue, is held constant with the damped balance beam.
Square Wave, 140 Volts DC
0
−0.5
0
Normal Force
Friction Force
Finger Velocity
20
22
24
26
Time (s)
28
30
Figure 4.4. Overview of raw friction data taken with tribometer
Velocity (mm/s)
Force (N)
0.5
31
Next, a 10 Hz sine wave was applied to the screen’s input, the results of which are shown in
figure 4.5. In figure 4.5a, the overview of one example swipe data is shown in the top plot. The
corresponding electrostatic actuation voltage is shown in the bottom subplot. Close inspection
of the normal and friction forces reveals time-varying electrostatic friction occurring at the
surface, shown in figure 4.5b. The electrostatic friction peaks at the same time the actuation
voltage reaches an extremum, and is therefore twice the frequency of the actuation signal. It
is important to notice that the measured normal force does not change. This is because the
electrostatic normal force is not measured by the load cell. What is measured is only the
pressing force on the finger, controlled by the counterweight applied to the balance beam. These
data support the force model in figure 4.1 and verify that electrostatic friction is measurable by
the tribometer.
Sine Wave, 140 Volts, 10 Hz
Sine Wave, 140 Volts, 10 Hz
0
Force (N)
0.5
0
0
−0.5
16
Time (s)
−50
0.3
Normal Force
Friction Force
0.1
Voltage (V)
Voltage (V)
0
14
0.4
0
200
−200
0.5
0.2
−40
Normal Force
Friction Force
Finger Velocity
−1.0
0.6
Force (N)
40
Velocity (mm/s)
1.0
18
(a) Overview of raw data with a 10 Hz sine wave
electrostatic effect.
200
14
14.5
15
14
14.5
Time (s)
15
0
−200
(b) Close-up of raw data during electrostatic
effect switch time. Supply voltage is 10 Hz,
friction is oscillatory at 20 Hz.
Figure 4.5. Electrostatic friction under low-frequency AC actuation
32
Square Wave, 140 Volts, 10 kHz
Force (N)
0.2
0
0
−0.2
Normal Force
Friction Force
Finger Velocity
−0.4
16
17
18
Velocity (mm/s)
50
0.4
−50
19
20
21
Time (s)
22
23
24
25
Figure 4.6. Overview of raw data taken for 10 kHz
In an attempt to maximize electrostatic friction, I experimented with high-frequency square
waves as actuation signals. Figure 4.6 shows raw measured forces for a 140 volt 10 kHz square
wave actuation signal. When the electrostatic actuation is turned on, during the gray portions
of the plot, the electrostatic friction force remains continuous. Because high-voltage square
waves yield consistently high electrostatic friction force, these signals are used to investigate
relationship between voltage and force.
4.4. Friction Analysis
The data obtained with the tribometer show that electrostatic friction is a clear increase in
friction force due to electrostatic actuation. With regard to haptics, this shows that electrostatics
is a valid actuation method for modulating friction force on a surface. However, the transduction between electric field and increased friction is not well understood. A friction model is a
mathematical relationship between the normal and lateral forces at the fingertip-glass contact.
I created a friction model for each subject based on data taken during the experiment. Using
these models, electrostatic friction can be attributed to an electrostatic attraction force between
the finger and screen.
33
To reduce the effect of measurement noise, I averaged the measured normal and friction
forces across constant velocity segments. Each swipe of the finger yields 4 data points, two in
each direction,which were used to extract electrostatic friction and pressing force. A sample
plot with the averaged data points is shown in figure 4.7. By repeating these measurements
Square Wave, 140 Volts, 10 kHz
Force (N)
0.2
0
0
−0.2
Normal Force
Friction Force
Finger Velocity
−0.4
16
17
18
Velocity (mm/s)
50
0.4
−50
19
20
21
Time (s)
22
23
24
25
Figure 4.7. Raw data with averaged data points shown
many times with different pressing forces applied, data points can be plotted on a friction versus
normal force plot, as shown in 4.8. This example shows one subject’s data for the 140 volt
10kHz square wave input tests. Two data groups appear, one for data points during which the
electrostatic force was present, and one for when it was not. The data taken when electrostatic
actuation is off provide a model for fingertip-surface friction, and the vertical difference between
data points represents electrostatic friction.
The fingertip-surface friction model shows a linear relationship between friction and normal
force, which follows the Coulombic model of dry friction. Performing a linear regression on these
data gives a friction coefficient, µ. Assuming µ itself does not change due to electric field, the
increase in friction force can be attributed to an electrostatic attraction force, inferred by the
34
Square Wave, 140 Volts, 10 kHz
0.5
Electrostatics On
Electrostatics Off
0.45
0.4
Friction Force (N)
0.35
0.3
Fe
0.25
∆F
0.2
0.15
0.1
0.05
0
0
0.2
0.4
0.6
Normal Force (N)
0.8
1
Figure 4.8. Sample lateral versus normal force plot of averaged data
equation:
(4.1)
Fe =
∆F
µ
where Fe is the electrostatic force, µ is the slope determined by regression, and ∆F is the average
electrostatic friction force. For each half-swipe of the finger, ∆F was calculated. Gathering
these values gives an average and a standard error, which are propagated through equation 4.1
resulting in an inferred electrostatic force and confidence interval. In the example figure 4.8, the
inferred electrostatic force is 250 mN, with a standard error of 70 mN.
35
As can be seen in the example figure 4.8, electrostatic friction increases slightly with increasing pressing force at the contact. This could be due to the area of contact increasing with the
pressing force. At higher pressing force measurements, the greater contact area contributes to
a bigger capacitive coupling between the finger and screen. More capacitance creates a greater
attraction force and a higher measured electrostatic friction.
36
CHAPTER 5
Analyzing Force and Electronics
The friction data have proven to be a very useful method for observing the effectiveness of an
electrostatic tactile display. Through friction analysis, I obtained a measure of the electrostatic
attraction force on the fingertip. Using the same technique in this chapter, I explore how that
force changes with respect to various actuation signals. The model derived in chapter 3 predicts
a square law relationship between force and actuation voltage, as well as a distinct dependence
on actuation frequency. I conducted two experiments, one to investigate actuation voltage and
one to investigate actuation frequency, to determine the validity of the model’s predictions.
37
5.1. Inferred Electrostatic Force Data
To compare the difference between electrostatic force at varying actuation voltages, I used
10 kHz square waves. I tested a total of seven subjects all using five different actuation voltages
at each of five different pressing forces. For each subject, I generated the plot shown in figure
4.8 for every actuation voltage for a total of thirty-five plots. Each plot provided an inferred
electrostatic force. A log-log plot of voltage versus electrostatic force for each subject is shown
in Figure 5.1a.
Sine Wave, 140 Volts
Square Wave, 10 kHz
0.18
0.30
Fe ∝ V
Inferred Electrostatic Force (N)
Inferred Electrostatic Force (N)
0.16
Subject Data
0.20
RC model prediction
Subject Data
2
0.10
0.05
0.03
0.02
0.01
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
60
80
100
120 140
Actuation Voltage (VA )
(a) Log-log plot of inferred electrostatic normal
force versus supply voltage at 10kHz square wave.
Subject data points have average standard error
of 45 mN. Mean exponent of the data 1.92, the
dashed line shows an exponent of 2.
102
103
104
Actuation Frequency (Hz)
(b) Inferred electrostatic normal force versus supply voltage frequency. Prediction does not model
the relationship properly. Subject data points have
average standard error of 37 mN.
Figure 5.1. Electrostatic force results
38
The actuation voltages shown in the plot are not equal to the force generation voltage, VF ,
because of the series capacitance in the cable of the 3M screen. Since all tests were done using
the same actuation signal, the impedance of the series capacitor in the cable does not change,
and the relationship between VA and VF is constant. While all subjects were able to detect
the transient change when electrostatics was switched on and off, the measured electrostatic
friction varied significantly across subjects. All subjects showed increasing force with voltage
relationship, the exponent of which was determined by fitting a line on a log scale. These
exponents are shown in figure 5.1a, and have mean 1.92. The data show very consistent square
law behavior over all seven subjects, and support the force model derived in chapter 3.
To assess the relationship between force and frequency, I used sine waves of 140 volt peak
amplitude. Because of the high-frequency components of square wave signals, sine waves must
be used when studying the frequency dependence of electrostatic force. All seven subjects were
tested under five different frequencies, equally spaced on a log scale from 100 Hz to 10 kHz.
The resulting experimental relationship between actuation frequency and electrostatic force is
shown in figure 5.1b. As the frequency of the signal increases, the force exhibited on the finger
increases as well, but it does not match the shape predicted by the simple dynamic RC model. To
examine this further, I measured the electrical impedance of each subject’s finger as a function
of frequency.
5.2. Fingertip Electric Characterization
The circuit model from chapter 3 predicted a first-order cutoff shown by the dashed line in
figure 5.1b. This prediction was based off the assumption that the stratum corneum acts as
a resistor and capacitor in parallel. I tested the validity of this assumption by measuring the
impedance of the finger part of the circuit. The finger was placed in direct contact with a flat
piece of copper connected to the source of an HP 35665A spectrum analyzer, as shown in 5.2a.
39
Rsense
Cu
HP 35665A
GND
Input
Source
Cu
(a) Measurement setup
Input
Rf
Rsense
Csc
Rsc
Source
(b) Circuit model
Figure 5.2. Finger impedance measurement experimental setup
The electrical schematic for this setup is shown in figure 5.2b. The spectrum analyzer measures
impedance vs. frequency by performing a sine sweep input and measuring the voltage across a
10 Ω current sensing resistor, labeled Rsense in the diagrams.
The impedance results on a bode plot are shown in figure 5.3. The dashed line represents the
theoretical impedance based on the model from chapter 3. The subjects displayed a wide variety
of impedance, ranging over two orders of magnitude. These data also show the shortcomings of
the first order RC model. All subject’s fingers again show a dependence on frequency, but at a
significantly more shallow slope than that of a first-order system, very similar to the behavior
observed with the electrostatic force. The phase information is also very important for analysis.
The dashed line shows the theoretical phase predicted by the simple linear model. The subject
data is very noisy, but shows that phase information does not follow that of the prediction. Both
the magnitude and phase plots resemble those of fractional-order models, such as those associated
40
107
Impedance (Ω)
106
105
104
103
102
101
102
103
104
105
104
105
20
Subject Data
RC Model
Phase (Deg)
0
−20
−40
−60
−80
−100
101
102
103
Frequency (Hz)
Figure 5.3. Finger impedance data
fractal combinations of first-order systems[24]. It is possible that the stratum corneum exhibits
fractional order behavior due to multi-scale effects, and so this must be considered in future
modeling work. All we can conclude from these impedance data now, though, is that a firstorder assumption for the electronics of the stratum corneum is insufficient.
41
CHAPTER 6
Conclusion
Electrostatic friction shows promise in the field of haptics with the capability of producing
lateral forces as high as 100 mN. This is similar to previously reported ultrasonically actuated
surface haptic displays. The ShiverPaD generated 100 mN of lateral force using low frequency
(∼ 800 Hz) in-plane vibrations[6], and the LateralPaD generated 70 mN of lateral force with
ultrasonic vibrations both in and out of plane[7]. These comparisons indicate that electrostatic
friction has potential applications not only in passive devices, but also in active devices, similar
to those described in [6] and [7].
Past studies[16] have called into question the model described in section 3.1. However, these
efforts operated under the assumption that human detection threshold was constant. My work
eliminates the human factor by directly measuring the interaction force at the surface-finger
interface. The data show a clear proportional relationship between electrostatic force and the
square of actuation voltage, strong evidence supporting equation 3.6. However, the dynamic RC
model from section 3.2 does not correctly model the relationship between electrostatic force and
actuation frequency. This inaccuracy is supported by both electrostatic force data and electrical
measurements of the finger.
6.1. Future Work
During the course of this work, a few issues arose that could be handled by improved experiment design. The tribometer uses a damped balance to control the pressing force. Since the
original objective was simply to keep the pressing force constant, the only way it can be changed
42
is by adding or removing counterweight to the balance. Because of this limitation, all electrostatic test cases are run for one pressing force, then the weight is changed and experiments are
repeated. This means that for each actuation signal test case, the data is taken at five different
times during the experiment, during which environmental conditions may have changed.
After analyzing my data, it’s clear that a friction model (lateral vs. normal force) should
be measured before each test case. A more ideal experimental procedure would be to sample
points on the friction curve without electrostatic actuation, then run a specific actuation signal
test case. This can be done by implementing pressing force control with an actuator and force
feedback. A friction model could be developed with more evenly sampled pressing force cases,
and be more relevant to finding the friction model at the time of a specific test case.
Improving the electrical modeling and measurement of the finger is a key direction for future
research. The first step is to remove the additional skin-electrode interface at the copper band
around the finger. By constructing a screen with two leads of opposite polarity under the finger,
the copper foil for grounding is not needed. The finger effectively closes the circuit between
the two different leads, and the body plays a significantly smaller role. With this approach,
measuring finger impedance becomes much simpler. A new electrical model is, of course, needed
to account for these changes. In addition, since electrostatic force is a direct result of charge,
an amplifier that controls charge rather than voltage would be better suited for actuation. A
current drive amplifier would generate a more consistent electrostatic force across subjects.
43
References
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[2] T. Nara, M. Takasaki, S. Tachi, and T. Higuchi, “An application of SAW to a tactile display
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[3] T. Watanabe and S. Fukui, “A method for controlling tactile sensation of surface roughness
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in Haptics Symposium, 2010 IEEE, pp. 317 –320, Mar. 2010.
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through variable friction reduction,” in EuroHaptics Conference, 2007 and Symposium on
Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics 2007.
Second Joint, pp. 421 –426, Mar. 2007.
[6] E. Chubb, J. Colgate, and M. Peshkin, “ShiverPaD: a glass haptic surface that produces
shear force on a bare finger,” IEEE Transactions on Haptics, vol. 3, pp. 189 –198, Sept.
2010.
[7] X. Dai, J. Colgate, and M. Peshkin, “LateralPaD: a surface-haptic device that produces
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Mar. 2012.
[8] J. Mullenbach, D. Johnson, J. Colgate, and M. Peshkin, “ActivePaD surface haptic device,”
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[10] J. Linjama and V. Mäkinen, “E-sense screen: Novel haptic display with capacitive electrosensory interface,” in HAID 2009, 4th Workshop for Haptic and Audio Interaction Design, (Dresden, Germany), 2009.
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[11] E. Mallinckrodt, A. L. Hughes, and W. Sleator, “Perception by the skin of electrically
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[12] S. Grimnes, “Electrovibration, cutaneous sensation of microampere current,” Acta Physiologica Scandinavica, vol. 118, no. 1, p. 19–25, 1983.
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[15] H. Tang and D. Beebe, “A microfabricated electrostatic haptic display for persons with
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[16] A. Agarwal, K. Nammi, K. Kaczmarek, M. Tyler, and D. Beebe, “A hybrid natural/artificial
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[17] K. Kaczmarek, K. Nammi, A. Agarwal, M. Tyler, S. Haase, and D. Beebe, “Polarity effect in
electrovibration for tactile display,” Biomedical Engineering, IEEE Transactions on, vol. 53,
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[18] T. Yamamoto and Y. Yamamoto, “Electrical properties of the epidermal stratum corneum,”
Medical and Biological Engineering and Computing, vol. 14, no. 2, pp. 151–158, 1976.
[19] Y. Yuan and R. Verma, “Mechanical properties of stratum corneum studied by nanoindentation,” MRS Online Proceedings Library, vol. 738, pp. null–null, 2002.
[20] K. Shima, Y. Tamura, T. Tsuji, A. Kandori, M. Yokoe, and S. Sakoda, “Estimation of
human finger tapping forces based on a fingerpad-stiffness model,” in Annual International
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[21] T. André, V. Lévesque, V. Hayward, P. Lefèvre, and J.-L. Thonnard, “Effect of skin hydration on the dynamics of fingertip gripping contact,” Journal of The Royal Society Interface,
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[22] T. Soneda and K. Nakano, “Investigation of vibrotactile sensation of human fingerpads by
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[23] S. Pasumarty, S. Johnson, S. Watson, and M. Adams, “Friction of the human finger pad:
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[24] H. Schiessel and A. Blumen, “Hierarchical analogues to fractional relaxation equations,”
Journal of Physics A: Mathematical and General, vol. 26, pp. 5057–5069, Oct. 1993.
46
APPENDIX A
Dynamic Analysis and Simulation of Touch Screen Circuit
The circuit diagram for the 3M MicroTouch screen and grounded fingertip is given in figure
A.1. The electrostatic force on the finger is static with respect to the voltage, VF , and charge
on the surface, Qf , but these values are dynamic with respect to the actuation voltage signal,
VA . A dynamic analysis is needed to predict an electrostatic force due to an arbitrary actuation
signal. Writing the Laplace transforms of the voltage and current laws gives:
Rf
Csc
Qf
Rsc
VA
VF
Ci
Cs
Figure A.1. Circuit diagram of the system
VA (s) =
0=
(A.1)
VF (s) =
Qt (s) Qt (s)
+
+ sQf (s)Rsc + sQt (s)Rf
Cs
Ci
Qsc (s)
− sQf (s)Rsc
Csc
Qt (s) Qsc (s)
+
Ci
Csc
Qf (s) = Qt (s) − Qsc (s)
47
Where Qf is the charge leakage through the stratum corneum, Qt is the total charge flowing
from the voltage source, and Qsc is the charge across the stratum corneum. Solving for those
charges yields the output relationships for the free charge Qf and Vf :
h
i
Cs + Csc Rsc (Ci + Cs ) s
(A.2)
VF (s)
i h
i
h
i
=h
VA (s)
Ci + Cs + Csc Rsc (Ci + Cs ) + Ci Cs (Rf + Rsc ) s + Ci Cs Csc Rf Rsc s2
Qf (s)
Ci Cs
i h
i
h
i
=h
VA (s)
Ci + Cs + Csc Rsc (Ci + Cs ) + Ci Cs (Rf + Rsc ) s + Ci Cs Csc Rf Rsc s2
To predict the resulting force on the finger, these systems are simulated in Matlab using the
function lsim. The signal Vs (t) is passed as an input to the system, and lsim returns VF (t) and
Qf (t). With these two signals determined, the force as a function of time is:
(A.3)
Fe (t) =
2
(Q (t))
2
Ci2 Csc
(VF (t))2 − fC 2
i
2Aε0 (Ci + Csc
)2
This force is, of course, periodic with the same frequency as the input, VA (t). The average of
this force over one period provides the prediction for the normal force exhibited by the finger.
48
APPENDIX B
Tribometer Control System Design and Electronics
All finger friction experiments were conducted using my custom-built tribometer. The tribometer is capable of electronically controlling finger velocity and haptic surface actuation signal.
A six-axis force torque sensor measures all contact forces, which are converted to analog voltages
by the ATI F/T Controller box. A PC104 stack controls all the peripheral devices to run the
experiment. All resources used for these experiments can be found on the LIMS server.
Function
Generator
PC104 (xPC)
Sensoray 526
RS232
Piezo Amp
TCP/IP
PC
(MATLAB)
F/T
Controller
Motor Amps
Motors
Encoders
Touch Screen
F/T
Sensor
Tribometer
Figure B.1. Tribometer control system schematic
49
B.1. PC104 Stack Control System
The PC104 Stack controls all real-time signals in the system, and records them to its memory
card. The table below shows the connections made to both the PC104 mainboard and the
Sensoray 526 data acquisition board. The RS232 connection controls both the function generator
and ATI controller. The force signals are carried by a shielded 4-wire cable to a D-Sub connector
on the back of the ATI controller. The colors of the signals are also reported in the table. A
ground connection between the function generator board and PC104 stack must also be made.
Table B.1. Connections to the PC104 stack
Mainboard
Function Generator
Local Router
Sensoray 526
AnalogIn-6
Z Force (Yellow)
AnalogIn-7
Y Force (Red)
AnalogIn-8
X Force (Green)
Analog Ground Analog Ground (Gray)
DigitalOut-1
Function Generator AnSwitch
AnalogOut-1
Function Generator AmpCtrl
AnalogOut-2
Finger Motor Amp
AnalogOut-3
Screen Stop Motor Amp
RS232-1
TCP/IP
The program for the xPC machine is written in the Simulink graphical language. The model
file is called FrictionMeasure.mdl, and can be run directly from Simulink by first connecting
to target and then executing real-time code. A graphical interface was written to simplify the
experimentation process, called FrictionMeasureGUI. Running this script automatically loads
the real-time code onto the xPC stack loads a control panel for running experiments. A screen
shot of the panel is shown in figure B.2.
Before starting the code, the screen should be in the level position, ready for calibration.
The large button in the upper left should be pressed first to start the real-time code. The three
F/T Sensor buttons reset the sensor, set the analog voltage ranges, and calibrate (set the zero)
50
Figure B.2. Graphical control interface for experiments
the sensor respectively. Lowering and raising the screen using the screen stop motor can be
done manually by pressing the respective buttons. The set wave and pulse ES selections are
used to generate a pulsing effect outside of an experiment. This is useful for demonstrating a
certain waveform and voltage before running an experiment. The waveform that will be tried is
determined by the appropriate voltage, frequency, and type selected in the experiment panel.
All experiments are run from the experiment panel. The subject name should read “TSXX”,
where XX is the subject number. The number of trials refers to the number of swipes recorded
for each experimental condition. Temperature and humidity are recorded as they are typed.
The three boxes below take either scalars or vectors, and all combinations of entered values will
51
be tested. For example, if the desired experiment is 140 volt sine waves of various frequencies
at 50 mm/s, then the boxes would read 50 mm/s, 140 V, and [100 1000 10000] Hz respectively.
B.2. Motor Amplifiers
The two motor amplifiers are linear current amplifiers. Each amplifier contains two Darlington pair transistors, one to control the current from the positive voltage rail, and one for the
negative. They are powered each by two floating DC power supplies, the negative terminal of
one connected to the positive terminal of the other to create positive and negative rails. The
common rail is connected to ground. An op-amp is used to control for current through the
sensing resistor, Rsense . A schematic and image of one of these amplifiers is provided below.
Cshunt
V+
1uF
Vin
10k
Rin
−
+
OPA177
Rbe
Cb
330
0.1uF
22
Rmotor
Cmotor
Cshunt
1uF
.22uF
Rif dbk
1k
Figure B.3. Linear motor amplifier schematic
V0.33
Rsense
52
Figure B.4. Linear motor amplifier
B.3. RS232 Function Generator
The function generator board is run by a PIC microcontroller that listens to RS232 commands from the PC104 stack. Depending on the content of the message, it either passes it along
to the ATI force sensor controller, or parses it to set waveform properties. The waveform is
created locally on the board using 4 integrated circuits. Using the Analog Devices AD9833 SPI
function generator chip, arbitrary sine, square, or triangle waves can be created with a command
from the PIC. Because the voltage out from the chip is unipolar and different amplitude for each
type of wave, a controllable amplitude correction circuit shown in Figure B.5 was developed. The
pairing of the Maxim MAX518 dual digital to analog converter(DAC) and the Analog Devices
AD633 linear multiplier is used for amplitude correction. The signal is first sent through a 10x
53
10kΩ
AD9833
PIC32
FuncOut
+15V
−
+
SPI
I2 C
1kΩ
-15V
MAX518
Vin
A0
A1
(Vin )(A0)
10
+ A1
AD633
Figure B.5. Function generator amplitude correction schematic
inverting amplifier before feeding into the multiplier chip. The multiplier chip then uses analog
values set by the DAC to properly adjust the signal levels. The values used are shown below
in table B.2. The output of the final AD633 chip is fed though another multiplier chip. The
Table B.2. Function generator amplitude correction settings (All values are voltages)
Wave Type FuncOut
Vin A0 A1
Vout
Square [0.0, 3.3] [−13, 0] 1.5
1 [−1, 1]
1 [−1, 1]
Sine [0.0, 0.6] [−6, 0] 3.5
Triangle [0.0, 0.6] [−6, 0] 3.5
1 [−1, 1]
multiplier is fed in from the PC104 stack analog output, as shown in table B.1. The purpose of
this multiplier is to allow for quick amplitude adjustments by the PC104. The signal is then fed
through an analog switch, the MAX 4526. This chip either grounds or passes the output signal
depending on the state of a digital input. This control signal is fed in from a digital output
on the PC104 stack, also shown in table B.1. Two 9-pin D-Sub connections are RS232 in from
the PC104 and out to the ATI force controller. A BNC connection carries the final conditioned
signal to the high-voltage piezo amplifier. Five volt power is supplied to the board from the
PC104 stack.