Ponce Wong Ruben D, Hellman Randall B, Santos Veronica J
IEEE Trans Haptics. 2014 Apr-Jun;7(2):191-202. doi: 10.1109/TOH.2013.56.
Upper-limb amputees rely primarily on visual feedback when using their prostheses to interact with others or objects in their environment. A constant reliance upon visual feedback can be mentally exhausting and does not suffice for many activities when line-of-sight is unavailable. Upper-limb amputees could greatly benefit from the ability to perceive edges, one of the most salient features of 3D shape, through touch alone. We present an approach for estimating edge orientation with respect to an artificial fingertip through haptic exploration using a multimodal tactile sensor on a robot hand. Key parameters from the tactile signals for each of four exploratory procedures were used as inputs to a support vector regression model. Edge orientation angles ranging from -90 to 90 degrees were estimated with an 85-input model having an R (2) of 0.99 and RMS error of 5.08 degrees. Electrode impedance signals provided the most useful inputs by encoding spatially asymmetric skin deformation across the entire fingertip. Interestingly, sensor regions that were not in direct contact with the stimulus provided particularly useful information. Methods described here could pave the way for semi-autonomous capabilities in prosthetic or robotic hands during haptic exploration, especially when visual feedback is unavailable.
上肢截肢者在使用假肢与他人或周围环境中的物体进行交互时，主要依赖视觉反馈。持续依赖视觉反馈可能会让人精神疲惫，而且在视线受阻时，许多活动仅靠视觉反馈是不够的。上肢截肢者若能仅通过触觉感知边缘（三维形状最显著的特征之一），将受益匪浅。我们提出了一种方法，通过在机器人手上使用多模态触觉传感器进行触觉探索，来估计相对于人工指尖的边缘方向。四个探索过程中每个过程的触觉信号关键参数被用作支持向量回归模型的输入。使用一个具有85个输入的模型估计了范围从-90度到90度的边缘方向角，该模型的R(2)为0.99，均方根误差为5.08度。电极阻抗信号通过对整个指尖上空间不对称的皮肤变形进行编码，提供了最有用的输入。有趣的是，未与刺激直接接触的传感器区域提供了特别有用的信息。这里描述的方法可为假肢或机器人手在触觉探索过程中的半自主能力铺平道路，尤其是在没有视觉反馈的情况下。
