The first in-depth study of a prosthetic finger tip with neural recordings of the human sense of touch has been published in two articles on Royal Society Interface Magazine. led the research professor Robotics and Artificial Intelligence (Ai), Nathan Lepora, from the Department of Mathematical Engineering at the University of Bristol, based in Bristol Robotics Laboratory. Machines can beat the best chess player in the world, but they can’t handle a chess piece like children. This lack of robot versatility is partly due to the fact that the artificial gripper lacks the precise touch sense of human fingertips, which is used to guide our hands while picking up and handling objects. “Our work helps discover how the complex internal structure of human skin creates our sense of touch – said Lebora – This is an exciting development in the field of soft robotics: the ability to 3D print tactile skin could create more skilled robots or dramatically improve the performance of prosthetic hands by giving them an integrated sense of touch. “
Professor Lepora and colleagues created the artificial finger’s sense of touch using a 3D-printed network of pin-shaped papillae on the underside of the skin that mimics the dermal papillae found between the outer and inner epidermal layers of human palpable skin. The papillae are made using advanced 3D printers that can mix soft and hard materials to create complex structures like those found in biology. “We have found that our 3D-printed tactile finger tip can produce artificial neural signals that are similar to recordings of real tactile neurons. – Libora explained Human tactile nerves transmit signals from different nerve endings, called mechanoreceptors, that can indicate the pressure and shape of the contact. The classic work of Phillips and Johnson in 1981 first mapped electrical recordings of these nerves to study “tactile spatial accuracy” using a series of standard waveforms used by psychologists. In our work, we tested 3D-printed prosthetic fingertips because they “felt” the same undulating shapes and found a surprisingly close match with the neural data. “
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