henhaobio
Sign in

Multisensory ResearchNeurosciencePeer-reviewed journal article

3-D Reconstruction of Fingertip Deformation during Contact Initiation

Donatien DoumontiD, Anika R. KaoiD, Julien LambertiD, françois wielantiD, Gregory J. GerlingiD, Benoit P. DelhayeiD, Philippe LefèvreiD

PDFDOIPreprint

Abstract

Dexterous manipulations rely on tactile feedback from the fingertips, which provides crucial information about contact events, object geometry, interaction forces, friction, and more. Accurately measuring skin deformations during tactile interactions can shed light on the mechanics behind such feedback. To address this, we developed a novel setup using 3-D digital image correlation (DIC) to both reconstruct the bulk deformation and local surface skin deformation of the fingertip under natural loading conditions. Here, we studied the local spatiotemporal evolution of the skin surface during contact initiation. We showed that, as soon as contact occurs, the skin surface deforms very rapidly and exhibits high compliance at low forces (<0.05 N). As loading and thus the contact area increases, a localized deformation front forms just ahead of the moving contact boundary. Consequently, substantial deformation extending beyond the contact interface was observed, with maximal amplitudes ranging from 5% to 10% at 5 N, close to the border of the contact. Furthermore, we found that friction influences the partial slip caused by these deformations during contact initiation, as previously suggested. Our setup provides a powerful tool to get new insights into the mechanics of touch and opens avenues for a deeper understanding of tactile afferent encoding.

Citations
4
FWCI
2.27
Field percentile
top 11%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISDoumont '26Schiltz '21Willemet '21Corniani '25Dunilac '22Delhaye '21
83% semantic matchNeuroscience