Inferring touch location on a handheld tool from mechanical vibrations
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2022-07-20
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en
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Abstract
Manipulating a tool, such as a simple rod, enables us to gather information from our
surroundings beyond the limits of our body: an example would be how we can
accurately sense the location of touch on the rod. Being able to differentiate sensory
information coming from a tool is theorized to be an important component of efficient
tool use; its behavioral and neural mechanisms, however, have only recently begun to
be characterized. Recent studies suggest high frequency mechanical vibrations
resulting from hitting with the rod play a role in perceiving touch location by
stimulating the mechanoreceptors of the hand. In the present thesis, both modeling
and experimental approaches were employed to investigate the contribution of
vibrations in tool-based sensing. We modeled the rod’s behavior based on the
Euler-Bernoulli beam theory and defined how vibrational properties change based on
the rod’s parameters and the location where the object was hit. We then used an
accelerometer array to successfully measure cutaneous vibrations during a tool use
experiment. Using the recorded vibrational patterns, we were able to model the
response of three types of tactile afferents (SA1, RA and PC). Finally, we applied a
support vector machine (SVM) based regression learning algorithm, which could
differentiate the mechanoreceptor response array on a continuous parameter space
with high accuracy. Our results suggest that the firing patterns of tactile afferents
encode location related information which cortical areas are able to differentiate.
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Faculteit der Sociale Wetenschappen