Stress Fluctuations in Sliding of Textured Objects and the Sense of Touch
Georges Debrégeas - Alexis Prevost
R. Candelier, J. Scheibert, S. Leurent
Laboratoire de Physique Statistique – ENS Paris
Patrice Rey (CEA-LETI)
Joël Frelat (LMM, Paris 6)
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Information transduction in tactile perception
Object + motion
SKIN DEFORMATIONS &
VIBRATIONS
NERVOUS
SIGNALS
REPRESENTATION
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Glabrous skin mechanoreceptors
Merkel's cell
complex
Ruffini
ending
Meissner's
corpuscule
Pacinian
corpuscule
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Glabrous skin mechanoreceptors
Stimulus
Stimulus
t
t
Slow Adaptation
Merkel's cell
complex
Ruffini
ending
Meissner's
corpuscule
Bolanowski et al., 1988
Fast Adaptation
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Pacinian
corpuscule
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Roughness perception: the duplex theory
Coding of coarse roughness
> ~200µm
SA I (Merkel) channel
 Resolution limited by the small receptive
field (few hundred µm)
 Spatial coding (static)
 Fairly independent of finger's motion

Coding of fine roughness
< ~200µm
Mediated by Pacinian corpuscules
exclusively
 Requires active tactile exploration
 Intensity coding

Hollins and Bensmaia, 2008
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Outline
Questions :
1 – How can one relate the physical properties of the object and
exploratory conditions to the mechanical signals experienced by
mechanoreceptive nerve endings.
2 – What are the consequences of this filtering process on the
transduction and neural encoding of tactile information.
1 – Biomimetic tactile sensing – design and calibration.
2 – Dynamic impulse response.
3 – Response to randomly rough substrates.
4 – A possible role for fingerprints.
5 – Conclusions and perspectives.
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The biomimetic approach
Real
finger
Artificial
finger
MEMS sensor
Sensitive area
Sensor
deth
Contact
diameter
Skin elastic
modulus
Human
fingertip
0.5 -10 mm
2-3 mm
~13mm
(P~0.5N)
1-4 MPa
Artificial
fingertip
2 mm
2.5 mm
~6mm
(P~1.5N)
2.2±0.1Mpa
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Response to a localized force
Indentation protocol:
Apply a ponctual
force at
on the
surface with a
rod.
Receptive fields measured by our MEMS sensors
Predicted receptive field for a ponctual sensor in a perfectly elastic material
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Response to a localized force
Indentation protocol:
Apply a ponctual
force at
on the
surface with a
rod.
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Response to a localized force
Indentation protocol:
Apply a ponctual
force at
on the
surface with a
rod.
Without exploration: roughly the same response for the 10 sensors
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A linear model for tactile transduction
Hertz contact
The stress felt by the sensor is given by:
+
Coulomb law:
+
Green function for a ponctual
force at the surface:
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A linear model for tactile transduction
Hertz contact
The stress at the sensor location reads:
+
Coulomb law:
+
Green function for a ponctual
force at the surface:
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Dynamic impulse response.
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Dynamic impulse response.
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Dynamic impulse response.
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Dynamic impulse response
u
Perturbation in force signal associated
with a small, isolated defect :
The modification in stress profile at the
interface reads
 s   s ( x ) ( x  u )
Perturbation in force signal for a sensor at x  x0 :
 s( u )   s ( x ) G( x  x0 )  ( x  u )
The response highly depends on the sensor's position within the contact zone
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Dynamic impulse response – normal stress
Left
Middle
Right
Experiment
Model
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Dynamic impulse response – tangential stress
Left
Middle
Right
Experiment
Model
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Midline profiles
Experiment
Model
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Receptive field variability in cortical neurons
DiCarlo et. al., 1998
The journal of Neuroscience
« The shape, area and strength of exitatory and inhibitory
receptive fields regions ranged widely. »
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Response to randomly rough substrates
Scanning over a binary patterned substrate
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The Volterra decomposition
The Volterra series is the analog of the
Taylor series, but for functionals:
The Volterra kernels
give a mapping from
to
.
NB: it is hard to extract the Volterra kernels ...
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The Wiener decomposition
For Gaussian white noise inputs, the Wiener kernels are orthogonal.
They can be computed through correlations:
...
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Extracting the linear kernel
g1 

measured
Predicted
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Consequences of skin patterning (e.g. fingerprints)
Artificial fingerprints
Square-wave gratings
(period 220mm)
on the skin's surface
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Smooth skin
Fingerprinted skin
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Linear model of mechanical transduction
Square wave gratings: T ( x )  1
Interfacial stress profile:  s ( u )   s ( x ).T ( x  u )
Force signal :
s( u )  s     s ( x ) G( x  x0 ).T ( x  u )dx
g1( x )
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Stimulus- signal response function
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Stimulus- signal response function
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Numerical illustration of the filtering process
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Numerical illustration of the filtering process
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Numerical illustration of the filtering process
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Numerical illustration of the filtering process
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Numerical illustration of the filtering process
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Consequence of fingerprints for fine texture perception
• Typical inter-ridge distance l ~ 500 µm
• « Natural » exploratory finger/substrate velocity
V ~ 10 cm/s
2 mm
• Frequency f = V / l ~ 200 Hz
• Order of the best frequency of Pacinian fibers
• Pacinian fibers = mediate the coding of fine texture
Scenario
 Fingerprints select one spatial frequency
 Velocity chosen to match Pacinian best response
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Conclusions
Biomimetic approach allowed to characterize the linear mechanical
transduction of texture information, and clarify the roles played by intrinsic
sensor’s response, interfacial contact stress field and skin topography.
But :
- Limited to binary topography.
- Non-linear effects should be important (stress coupling within the contact
zone, normal stress dependence of the friction coefficient, etc.) Reverse
correlation should allow to probe that.
Important open question :
How does the tactile system deal with such context dependent variability of
individual sensors’ response. What encoding strategies may yield a stable
representation of the probed surface.
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Comparing biomimetic and human touch
Can one relate the subcutaneous stress field measured with
the biomimetic sensor with actual neurographic data ?
JP Roll - LNH – Marseille
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Comparing whisker and digital touch
Dan Shulz – Yves Boubenec
UNIC - Gif-sur-Yvette
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Rodents whisker touch
A4
A1
A3 A2



Daniel Shulz (Gif-CNRS)

Wolfe & Feldman, ‘08
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F
Johnson & Phillips, 1981
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The cochlea
Georg von Békésy, ‘47
inner hair cells
outer hair cells
Nobili, Mammano and Ashmore, ‘98
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…Back to the actual finger
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Normalized power spectrum
Normalized power spectrum
Can we see this effect on a real finger ?
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Communicative & Integrative Biology,
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