Haptogram: Ultrasonic Point-Cloud Tactile Stimulation
Haptogram: Ultrasonic Point-Cloud Tactile Stimulation
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DOI：
10.1109/access.2016.2608835
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发表时间：
2016-01-01
期刊：
影响因子：
3.9
通讯作者：
Eid, Mohamad
中科院分区：
文献类型：
--
作者：
Korres, Georgios;Eid, Mohamad
超声作为一种触觉显示的刺激效应的研究最近在触觉领域变得更加深入。在本文中，我们介绍了Haptogram的设计、开发和评估；Haptogram是一个通过声辐射压力提供点云触觉显示的系统。平铺的二维超声换能器阵列被用来产生一个焦点，该焦点被制作成动画，以产生任意的二维和三维触觉形状。切换速度非常快，让人同时感受到分布的点数。该图形系统包括软件组件和硬件组件。该软件组件使用户能够创作和/或选择触觉对象，创建点云表示，并生成一系列焦点以驱动硬件。硬件组件包括一个平铺的二维超声换能器阵列，每个换能器由一个FPGA驱动。定量分析是为了测量触觉图显示各种触觉形状的能力，包括单点、二维形状(直线和圆形)和三维对象(半球)。结果表明，所有显示的触觉对象都是人类皮肤可以感知的(200个焦点的平均压力为2.65kpa)。还进行了一项可用性研究，以评估人类识别2-D形状的能力。结果表明，识别率远高于机会水平(平均59.44%，标准差12.75%)，识别时间平均为13.87%S(标准差3.92%S)。
Studies of the stimulating effect of ultrasound as a tactile display have recently become more intensive in the haptic domain. In this paper, we present the design, development, and evaluation of Haptogram; a system designed to provide point-cloud tactile display via acoustic radiation pressure. A tiled 2-D array of ultrasound transducers is used to produce a focal point that is animated to produce arbitrary 2-D and 3-D tactile shapes. The switching speed is very high, so that humans feel the distributed points simultaneously. The Haptogram system comprises a software component and a hardware component. The software component enables users to author and/or select a tactile object, create a point-cloud representation, and generate a sequence of focal points to drive the hardware. The hardware component comprises a tiled 2-D array of ultrasound transducers, each driven by an FPGA. A quantitative analysis is conducted to measure the Haptogram ability to display various tactile shapes, including a single point, 2-D shapes (a straight line and a circle) and a 3-D object (a hemisphere). Results show that all displayed tactile objects are perceivable by the human skin (an average of 2.65 kPa for 200 focal points). A usability study is also conducted to evaluate the ability of humans to recognize 2-D shapes. Results show that the recognition rate was well above the chance level (average of 59.44% and standard deviation of 12.75%) while the recognition time averaged 13.87 s (standard deviation of 3.92 s).