TY - GEN
T1 - A comprehensive mechanotransduction model for tactile feedback based on multi-axial stresses at the fingertip-contact interface
AU - Valero, M. R.
AU - Hale, N.
AU - Tang, J.
AU - Jiang, L.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - This paper presents a mechanotransduction model designed to convert the multi-axial mechanical loads at the fingertip-contact interface into neural-spike trains, the MultiAxial Stress Mechanotransduction (MASM) model. Seeking a comprehensive solution and more direct integration with sensor systems in tactile applications, the model accounts for the conversion of multi-axial (pressure and shear) stresses at the fingertip-contact interface into spike trains with artificial slow adapting (SA) and rapidly adapting (RA) afferents type I (SAI, RAI) and II (SAII, RAII). These have been modelled based on the properties of those in human fingertips. To illustrate how the model works, artificial data mimicking typical stress stimuli profiles used to evaluate the response of biological afferents were fed to the model and results examined. Subsequently, the suitability of the model for real tactile applications was preliminary tested by inputting to the model real life, measured pressure and shear data in a fingertip 'press-push-lift' action. The response of the modelled afferents was analyzed and qualitatively compared to typical responses of biological units. Initial results show that it is possible to codify the mechanical contact tactile information measured by multi-axial sensor systems in a bio-inspired fashion, reproducing relevant features similar to those produced by biological mechanoreceptors.
AB - This paper presents a mechanotransduction model designed to convert the multi-axial mechanical loads at the fingertip-contact interface into neural-spike trains, the MultiAxial Stress Mechanotransduction (MASM) model. Seeking a comprehensive solution and more direct integration with sensor systems in tactile applications, the model accounts for the conversion of multi-axial (pressure and shear) stresses at the fingertip-contact interface into spike trains with artificial slow adapting (SA) and rapidly adapting (RA) afferents type I (SAI, RAI) and II (SAII, RAII). These have been modelled based on the properties of those in human fingertips. To illustrate how the model works, artificial data mimicking typical stress stimuli profiles used to evaluate the response of biological afferents were fed to the model and results examined. Subsequently, the suitability of the model for real tactile applications was preliminary tested by inputting to the model real life, measured pressure and shear data in a fingertip 'press-push-lift' action. The response of the modelled afferents was analyzed and qualitatively compared to typical responses of biological units. Initial results show that it is possible to codify the mechanical contact tactile information measured by multi-axial sensor systems in a bio-inspired fashion, reproducing relevant features similar to those produced by biological mechanoreceptors.
UR - http://www.scopus.com/inward/record.url?scp=85034271653&partnerID=8YFLogxK
U2 - 10.1109/WHC.2017.7989874
DO - 10.1109/WHC.2017.7989874
M3 - Conference Contribution (Conference Proceeding)
AN - SCOPUS:85034271653
T3 - 2017 IEEE World Haptics Conference, WHC 2017
SP - 42
EP - 47
BT - 2017 IEEE World Haptics Conference, WHC 2017
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 7th IEEE World Haptics Conference, WHC 2017
Y2 - 6 June 2017 through 9 June 2017
ER -