  Our research investigates vibrotactile perception in four prosthetic hands
with distinct kinematics and mechanical characteristics. We found that rigid
and simple socket-based prosthetic devices can transmit tactile information and
surprisingly enable users to identify the stimulated finger with high
reliability. This ability decreases with more advanced prosthetic hands with
additional articulations and softer mechanics. We conducted experiments to
understand the underlying mechanisms. We assessed a prosthetic user's ability
to discriminate finger contacts based on vibrations transmitted through the
four prosthetic hands. We also performed numerical and mechanical vibration
tests on the prostheses and used a machine learning classifier to identify the
contacted finger. Our results show that simpler and rigid prosthetic hands
facilitate contact discrimination (for instance, a user of a purely cosmetic
hand can distinguish a contact on the index finger from other fingers with 83%
accuracy), but all tested hands, including soft advanced ones, performed above
chance level. Despite advanced hands reducing vibration transmission, a machine
learning algorithm still exceeded human performance in discriminating finger
contacts. These findings suggest the potential for enhancing vibrotactile
feedback in advanced prosthetic hands and lay the groundwork for future
integration of such feedback in prosthetic devices.
