This paper presents a mechanotransduction model designed to convert the multi-axial mechanical loads at the fingertip-contact interface into neural-spike trains, the Multi-Axial Stress Mechanotransduction (MASM) model. We believe this is a first attempt towards a comprehensive model, accounting for the conversion of measured multi-axial (pressure and shear) stresses at the fingertip-contact interface into spike trains with modelled slow adapting (SA) and rapidly adapting (RA) afferents type I

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... AI, RAI) and II (SAII, RAII) based on the properties of those in human fingertips. To illustrate and assess how the MASM model works, artificial data mimicking typical stress stimuli used to evaluate the response of biological afferents were fed to the model and results examined. Subsequently, the suitability of the MASM 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 SA and RA afferents was analysed and qualitatively compared to biological data reported in literature. 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, thus reproducing relevant features similar to those produced by biological mechanoreceptors.