Intent Seeking Algorithms for New Human-Machine Interface

主要研究者：Joshi，Sanjay S.提案编号：0966963项目摘要：我们为瘫痪患者创建了一种新颖的人机界面（HMI）技术，该技术使用单个面部肌肉的表面肌电图（sEMG）信号来同时多维控制外部设备。我们的新控制器有可能显著提高用户的生活质量。不像许多现有的人机界面为这一人群，我们的界面是：不显眼的不显眼，不干扰眼睛/嘴/舌头，连续可用，当需要时，多功能，易于使用，在几乎任何头部位置，和便携式。我们最近发现，人类可以学习如何同时操纵sEMG功率谱的两个独立频带中的功率水平（只需收缩肌肉）。每个频带变成单独的控制信道，其可以同时控制设备的不同方面。因此，我们可以利用一个单一的肌肉？的自然电信号的方式比以前所知的复杂得多。利用这一潜在的发现，我们已经开发出一种新的用户界面，依赖于一个单一的头部肌肉？的表面肌电信号，这是很容易获得和限制到一个小的非描述性区域附近的耳朵。我们的系统有点类似于一些基于脑电图（EEG）的脑机接口（BCI），其中一个人学会？导游？在计算机屏幕上的特定位置上的光标。屏幕上的这些位置可以是虚拟按钮，用于打开计算机应用程序（人机界面），打开/关闭灯（环境控制单元）或控制轮椅（移动应用程序）。两大核心挑战？光标引导？HMI系统是1）意图（计算机如何知道用户打算将光标放置在哪里？），以及2）光标可以达到预期位置的速度。这两个问题交织在一起，因为早期的意图知识可以导致更快的系统。我们建议开发新的？意图寻求？这些算法可以使我们的HMI比我们目前的实例化系统快得多。此外，为了对不能离开家或医院的残疾人进行评估研究，我们将开发一种新的更小的移动的版本的硬件，它非常容易在任何地方运输，设置和使用。智力优势：在人机界面中使用单个sEMG信号进行同时多维控制具有潜在的变革性。预测目标（在这种情况下是计算机光标）未来位置的概念出现在许多不同的应用中（例如航空航天工程，机器人，脑机接口）。这些应用程序采用数学和计算机科学技术的组合，包括统计决策，最佳过滤和人工智能。我们打算从这些领域中借鉴，为我们的人机界面应用开发准确，快速的算法。从硬件的角度来看，出现了全新类别的计算设备，它们可以从手持设备执行复杂的计算并运行图形密集型应用程序。（或更小）的占地面积。围绕这些操作平台设计我们的界面将推进高度便携和易于使用的辅助界面领域。更广泛的影响：里夫基金会（Reeve Foundation）最近发起的一项研究（2009年）估计，美国有超过550万人患有瘫痪。许多最严重的瘫痪患者使用呼吸器呼吸，并在一天中的不同时间限制在某些头部/身体位置。我们的目标是让严重瘫痪的人重新获得对周围环境的控制和基本的独立性。我们致力于将残疾人纳入我们的研究，不仅作为研究对象，而且作为研究人员本身。因此，我们的工作将在研究包容性方面产生更广泛的影响。就更广泛的智力影响而言，我们的新意图搜索算法可以应用于许多计算机操作系统/程序，残疾人或非残疾人使用各种设备在屏幕上引导光标。

PI: Joshi, Sanjay S.Proposal Number: 0966963Project Summary: We have created a novel human-machine interface (HMI) technology for paralyzed persons which uses the surface electromyography (sEMG) signal of a single, facial muscle for simultaneous multidimensional control of external devices. Our new controller has the potential to significantly increase the quality of life for its users. Unlike many existing humancomputer interfaces for this population, our interface is: unobtrusive & inconspicuous, noninterfering with eyes/mouth/tongue, continuously available when needed, multifunctional, easy-to-use in almost any head position, and portable. We have recently discovered that humans can learn how to simultaneously manipulate power levels in two separate frequency-bands of a sEMG power spectrum (simply by contracting the muscle). Each frequency band becomes a separate control channel, which can simultaneously control different aspects of a device. Thus, we may exploit a single muscle?s natural electrical signals in far more complex ways than previously known. Using this underlying discovery, we have developed a new user interface that relies on a single head muscle?s surface EMG signal, which is easy to obtain and restricted to a small non-descript area near the ear. Our system is somewhat similar to some electroencephalographic (EEG) based brain-computer interfaces (BCI) in which a person learns to ?guide? a cursor to certain positions on a computer screen. These positions on the screen could be virtual buttons that open computer applications (human-computer interfaces), turn on/off lights (environmental control units), or control wheelchairs (mobility applications). Two central challenges in all ?cursor-guided? HMI systems are 1) intent (how does the computer know where the user intended to place the cursor?), and 2) speed at which the cursor can achieve the intended position. These two questions are intertwined in that earlier knowledge of intent can lead to faster systems. We propose to develop new ?intent-seeking? algorithms that could make our HMI much faster than our currently instantiated system. In addition, in order to conduct evaluation studies on subjects with disabilities who cannot leave either home or hospital, we will develop a new smaller mobile version of our hardware that is very easy to transport, setup, and use anywhere.Intellectual Merit: The use of a single sEMG signal for simultaneous multidimensional control in human-computer interfaces is potentially transformative. The notion of predicting the future location of a target (in this case a computer cursor) arises in many different applications (e.g. aerospace engineering, robotics, brain-computer interfaces). These applications employ a combination of mathematical and computer-science techniques including statistical decision making, optimal filtering, and artificial intelligence. We intend to draw from these fields to develop accurate, fast algorithms for our human-computer interface application. From a hardware perspective, entire new classes of computing devices are appearing that can perform complex computations and run graphics-intensive applications from a hand-held (or smaller) footprint.Designing our interface around these operating platforms will advance the area of highly portable and easy-to-use assistive interfaces.Broader Impact: A recent study initiated by the Reeve Foundation (2009) estimates that more than 5.5 million people live with paralysis in the United States. Many of the most severely paralyzed use ventilators to breathe, and are confined to certain head/body positions at different times during the day. Our goal is for severely paralyzed persons to regain some control of their surroundings and some basic independence. We are committed to including disabled persons in our research, not only as subjects but also as researchers themselves. As such, our work will create an additional broader impact in terms of research inclusiveness. In terms of intellectual broader impact, our new intent-seeking algorithms could have applications for many computer operating systems/programs for which disabled or non-disabled persons use various devices to guide cursors on a screen.