Fingertip haptic feedback offers advantages in many applications, including robotic teleoperation, gaming, and training. However, fingertip size and shape vary significantly across humans, making it difficult to design fingertip interfaces and rendering techniques suitable for everyone. This article starts with an existing data-driven haptic rendering algorithm that ignores fingertip size, and it then develops two software-based approaches to personalize this algorithm for fingertips of different sizes using either additional data or geometry. We evaluate our algorithms in the rendering of pre-recorded tactile sensations onto rubber casts of six different fingertips as well as onto the real fingertips of 13 human participants. Results on the casts show that both approaches significantly improve performance, reducing force error magnitudes by an average of 78% with respect to the standard non-personalized rendering technique. Congruent results were obtained for real fingertips, with subjects rating each of the two personalized rendering techniques significantly better than the standard non-personalized method.

中文翻译：

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补偿指尖大小以更准确地呈现触觉提示

指尖触觉反馈在许多应用中具有优势，包括机器人遥操作、游戏和培训。然而，指尖大小和形状因人而异，因此很难设计出适合所有人的指尖界面和渲染技术。本文从忽略指尖大小的现有数据驱动触觉渲染算法开始，然后开发了两种基于软件的方法，使用附加数据或几何形状为不同大小的指尖个性化该算法。我们在将预先记录的触觉渲染到六个不同指尖的橡胶模型以及 13 位人类参与者的真实指尖上来评估我们的算法。演员表的结果表明，这两种方法都显着提高了性能，相对于标准的非个性化渲染技术，力误差幅度平均减少 78%。真实指尖获得了一致的结果，受试者对两种个性化渲染技术中的每一种的评价都显着优于标准的非个性化方法。