Intent Seeking Algorithms for New Human-Machine Interface

项目摘要：我们为瘫痪患者创造了一种新的人机界面(HMI)技术，它使用单个面部肌肉的表面肌电(SEMG)信号来同时对外部设备进行多维控制。我们的新控制器有可能显著提高其用户的生活质量。与许多现有的面向这一人群的人机界面不同，我们的界面是：不显眼，不干扰眼睛/嘴/舌头，需要时可连续使用，多功能，几乎在任何头部位置都易于使用，并且便携。我们最近发现，人类可以学习如何同时控制表面肌电信号功率谱中两个不同频段的能量水平(只需收缩肌肉)。每个频段成为单独的控制通道，可以同时控制设备的不同方面。因此，我们可以用比我们所知的复杂得多的方式来利用单个肌肉--S的自然电信号。利用这一潜在的发现，我们开发了一种新的用户界面，它依赖于单一的头部肌肉-S表面肌电信号，它很容易获得，并且局限于耳朵附近的一个小的非描述性区域。我们的系统有点类似于一些基于脑电(EEG)的脑机接口(BCI)，在这种接口中，一个人学习如何引导？指向计算机屏幕上某一位置的光标。屏幕上的这些位置可以是打开计算机应用程序(人机界面)、开/关灯(环境控制单元)或控制轮椅(移动应用程序)的虚拟按钮。总共有两个核心挑战？光标导引？HMI系统有1)意图(计算机如何知道用户打算将光标放在哪里？)，以及2)光标达到预期位置的速度。这两个问题交织在一起，因为对意图的更早了解可以导致更快的系统。我们建议开发新的？意图寻觅？可以使我们的人机界面比我们目前实例化的系统快得多的算法。此外，为了对既不能离开家也不能离开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版本的硬件，非常容易运输、设置和在任何地方使用。智能优点：使用单个表面肌电信号在人机界面中同时进行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)的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-计算机接口)。这些应用程序结合了数学和计算机科学技术，包括统计决策、最优过滤和人工智能。我们打算从这些领域中吸取教训，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，全新的计算设备类别正在出现，它们可以执行复杂的计算，并从手持(或更小的)占地面积运行图形密集型应用程序。围绕这些操作平台设计我们的界面将推动高度便携和易于使用的辅助界面的领域。广泛影响：里夫基金会(2009)最近发起的一项研究估计，美国有超过550万人生活在瘫痪之中。许多瘫痪最严重的人使用呼吸机呼吸，并在一天中的不同时间被限制在特定的头部/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一些控制和一些基本的独立。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额外的更广泛的影响。就智力的更广泛影响而言，我们新的意图寻找算法可能适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来引导屏幕上的光标。

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.