The University of Auckland

Project #106: Tactile perception studied with 3D printing and motion capture

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Description:

This is a biomechatronics project.

You'll use 3D printing and motion capture to better understand how the human nervous system processes forms and shapes that contact the finger tip.

In previous work, we 3D-printed test objects comprising a regular, rectangular array of raised, oriented bars measuring 0.75 × 0.75 × 3 mm (width × height × length) that were centre-to-centre spaced by 4 mm. Bars on the left-hand-side of a test object were horizontal, and those on the right were vertical, thus defining a texture boundary. We independently jittered the orientations of bars by drawing random numbers from a uniform distribution; across trials, we systematically increased jitter from 0° (i.e., no jitter) to ±90° (i.e., no boundary). Blindfolded participants (n = 25) used the preferred index finger pad to actively scan objects for 10 seconds before reporting the texture boundary's orientation (vertical or horizontal; randomised across trials). Results showed a threshold jitter of ±52.7° (i.e., the jitter at which the boundary orientation was only just discriminable). We also developed a mechanistic model of the early stages of tactile processing in the nervous system. This model indicated that the first stage of processing is like a tuned filter with spatial frequency = 0.23 cycles per mm and spatial extent = 2.53 mm (full-width at half-maximum).

Your job is to take this work to the next level.

Your starting points are here: [1] McLean, C. F., Trew, D. J., & Hallum, L. E. (2023). A filter-rectify-filter model of the tactile perception of 3D-printed, texture-defined form. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/EMBC40787.2023.10341026 [2] Killebrew, J. H., Bensmaia, S. J., Dammann, J. F., Denchev, P., Hsiao, S. S., Craig, J. C., & Johnson, K. O. (2007). A dense array stimulator to generate arbitrary spatio-temporal tactile stimuli. Journal of neuroscience methods, 161(1), 62-74.

Keywords: biomechatronics, 3D printing, tactile perception

 

Type:

Undergraduate

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