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CHS: Small: Rich Surface Interaction for Augmented Environments

CHS：小型：增强环境的丰富表面交互

基本信息

批准号：
1909089
负责人：
Keith Vertanen
金额：
$ 49.96万
依托单位：
Michigan Technological University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909089&HistoricalAwards=false
关键词：
CHS Small Rich Surface Interaction

项目摘要

Virtual Reality (VR) and Augmented Reality (AR) head-mounted displays are increasingly being used in different computing related activities such as data visualization, education, and training. Currently, VR and AR devices lack efficient and ergonomic ways to perform common desktop interactions such as pointing-and-clicking and entering text. The goal of this project is to transform flat, everyday surfaces into a rich interactive surface. For example, a desk or a wall could be transformed into a virtual keyboard. Flat surfaces afford not only haptic feedback, but also provide ergonomic advantages by providing a place to rest your arms. This project will develop a system where microphones are placed on surfaces to enable the sensing of when and where a tap has occurred. Further, the system aims to differentiate different types of touch interactions such as tapping with a fingernail, tapping with a finger pad, or making short swipe gestures. This project will investigate different machine learning algorithms for producing a continuous coordinate for taps on a surface along with associated error bars. Using the confidence of sensed taps, the project will investigate ways to intelligently inform aspects of the user interface, e.g. guiding the autocorrection algorithm of a virtual keyboard decoder. Initially, the project will investigate sensing via an array of surface-mounted microphones and design "surface algorithms" to determine and compare the location accuracy of the finger taps on the virtual keyboard. These algorithms will experiment with different models including existing time-of-flight model, a new model based on Gaussian Process Regression, and a baseline of classification using support vector machines. For all models, the project will investigate the impact of the amount of training data from other users, and varying the amount of adaptation data from the target user. The project will compare surface microphones with approaches utilizing cameras and wrist-based inertial sensors. The project will generate human-factors results on the accuracy, user preference, and ergonomics of interacting midair versus on a rigid surface. By examining different sensors, input surfaces, and interface designs, the project will map the design space for future AR and VR interactive systems. The project will disseminate software and data allowing others to outfit tables or walls with microphones to enable rich interactive experiences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

虚拟现实（VR）和增强现实（AR）头戴式显示器越来越多地用于不同的计算相关活动，如数据可视化、教育和培训。目前，VR和AR设备缺乏高效和符合人体工程学的方式来执行常见的桌面交互，如点击和输入文本。该项目的目标是将平坦的日常表面转化为丰富的互动表面。例如，桌子或墙壁可以变成虚拟键盘。平坦的表面不仅提供触觉反馈，而且提供了一个休息手臂的地方，从而提供了符合人体工程学的优势。这个项目将开发一个系统，在这个系统中，麦克风被放置在物体表面上，从而能够感应到敲击发生的时间和地点。此外，该系统旨在区分不同类型的触摸交互，如用指甲敲击，用手指垫敲击，或做出简短的滑动手势。该项目将研究不同的机器学习算法，以产生表面上水龙头的连续坐标以及相关的误差条。利用感应敲击的信心，该项目将研究如何智能地通知用户界面的各个方面，例如指导虚拟键盘解码器的自动校正算法。最初，该项目将通过一系列表面安装的麦克风来研究感应，并设计“表面算法”，以确定和比较手指敲击虚拟键盘的位置准确性。这些算法将实验不同的模型，包括现有的飞行时间模型，基于高斯过程回归的新模型，以及使用支持向量机的分类基线。对于所有模型，该项目将调查来自其他用户的训练数据量的影响，并改变来自目标用户的适应数据量。该项目将比较地面麦克风与利用摄像头和腕部惯性传感器的方法。该项目将在准确性、用户偏好和人机工程学方面产生人为因素的结果，以在空中与刚性表面上进行交互。通过检查不同的传感器、输入表面和界面设计，该项目将为未来的AR和VR交互系统绘制设计空间。该项目将传播软件和数据，允许其他人在桌子或墙壁上安装麦克风，以实现丰富的互动体验。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。