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.

翻译：本研究探究了四种具有不同运动学特性和力学特性的假手在振动触觉感知方面的表现。我们发现，刚性和简单套筒式假肢装置能够传递触觉信息，并且令人惊讶地使使用者能够高可靠地识别受刺激的手指。这种能力会随着关节更多、力学结构更柔性的先进假手而降低。我们通过实验来理解其内在机制。我们评估了假肢使用者基于通过四种假手传递的振动区分手指接触点的能力。我们还对假肢进行了数值和机械振动测试，并使用机器学习分类器识别被接触的手指。结果表明，结构更简单、更刚性的假手有利于接触辨别（例如，纯美容手的使用者区分食指与其他手指接触的准确率可达83%），但所有测试的假手（包括柔性先进的）的表现均高于随机水平。尽管先进假手会降低振动传递，但机器学习算法在区分手指接触方面的表现仍优于人类。这些发现表明，增强先进假手振动触觉反馈具有潜力，并为未来在假肢装置中整合此类反馈奠定了基础。