Stress Fluctuations in Sliding of Textured Objects and the Sense of Touch

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Tribology Gordon Conference 2010

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)

Information transduction in tactile perception

Object + motion

SKIN DEFORMATIONS &VIBRATIONS

NERVOUSSIGNALS

REPRESENTATION

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Glabrous skin mechanoreceptors

Merkel's cellcomplex

Meissner'scorpuscule

Pacinian corpuscule

Ruffiniending

Glabrous skin mechanoreceptors

Stimulus

Slow Adaptation

Fast Adaptation

Merkel's cellcomplex

Meissner'scorpuscule

Pacinian corpuscule

Ruffiniending

Stimulus

Bolanowski et al., 1988

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

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.

Outline

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.

7Tribology Gordon Conference 2010

The biomimetic approach

Realfinger

Artificial finger

Sensitive area Sensor deth

Contact diameter

Skin elastic modulus

Humanfingertip

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

MEMS sensor

Response to a localized force

Indentation protocol:Apply a ponctualforce at on the surface with a rod.

Receptive fields measured by our MEMS sensorsPredicted receptive field for a ponctual sensor in a perfectly elastic material

Without exploration: roughly the same response for the 10 sensors

A linear model for tactile transduction

+Coulomb law:

Green function for a ponctual force at the surface:

Hertz contact

The stress felt by the sensor is given by:

A linear model for tactile transduction

+Coulomb law:

Green function for a ponctual force at the surface:

Hertz contact

The stress at the sensor location reads:

Dynamic impulse response.

Dynamic impulse response

Perturbation in force signal associated with a small, isolated defect :

The modification in stress profile at the interface reads

Perturbation in force signal for a sensor at :

The response highly depends on the sensor's position within the contact zone

)ux()x(ss

)ux()xx(G)x()u(s s 0

Dynamic impulse response – normal stress

Experiment

Middle RightLeft

Dynamic impulse response – tangential stress

Experiment

Middle RightLeft

Midline profiles

Experiment

Receptive field variability in cortical neurons

DiCarlo et. al., 1998The journal of Neuroscience

« The shape, area and strength of exitatory and inhibitory receptive fields regions ranged widely. »

Response to randomly rough substrates

Scanning over a binary patterned substrate

The Volterra kernels give a mapping from to .

The Volterra decomposition

The Volterra series is the analog of the Taylor series, but for functionals:

NB: it is hard to extract the Volterra kernels ...

For Gaussian white noise inputs, the Wiener kernels are orthogonal.

The Wiener decomposition

They can be computed through correlations:

Extracting the linear kernel

measured

Predicted

Consequences of skin patterning (e.g. fingerprints)

Artificial fingerprintsSquare-wave gratings

(period 220mm)on the skin's surface

Smooth skinFingerprinted skin

Linear model of mechanical transduction

dx)ux(T).xx(G)x(s)u(s s 0

Square wave gratings:

)ux(T).x()u( ss

Interfacial stress profile:

Force signal :

Stimulus- signal response function

Numerical illustration of the filtering process

• Typical inter-ridge distance l ~ 500 µm• « Natural » exploratory finger/substrate velocity V ~ 10 cm/s

• 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

Consequence of fingerprints for fine texture perception

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.

Comparing biomimetic and human touch

JP Roll - LNH – Marseille

Can one relate the subcutaneous stress field measured with the biomimetic sensor with actual neurographic data ?

Comparing whisker and digital touch

Dan Shulz – Yves BoubenecUNIC - Gif-sur-Yvette

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Daniel Shulz (Gif-CNRS)

A 1A 2A 3A 4

Rodents whisker touch

Wolfe & Feldman, ‘08

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Johnson & Phillips, 1981

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The cochlea

Georg von Békésy, ‘47

inner hair cellsouter hair cells

Nobili, Mammano and Ashmore, ‘98

…Back to the actual finger

Can we see this effect on a real finger ?

0.01 0.1 1 10Nor

1/u (mm-1)0

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2 10-5

3 10-5

4 10-5

5 10-5

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1/u (mm-1)

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0 0.5 1 1.5 2<F> (N)

Communicative & Integrative Biology, 2009

Stress Fluctuations in Sliding of Textured Objects and the Sense of Touch

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