Intelligent systems for pediatric rehabilitation

儿科康复智能系统

• 批准号: RGPIN-2014-06077
• 负责人: Chau, Tom
• 金额: $ 3.72万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=588797
• 关键词: Intelligent; systems; pediatric; rehabilitation

CHALLENGE Many tens of thousands of Canadian children and youth with severe disabilities still do not have a means of communication despite the advent of numerous technologies that provide alternative access to computers and communication devices. These so-called access technologies tap into everything from muscle activations to brain waves. However, existing technologies fall short in part because they are unaware of the user’s emotional or mental state. For example, when a user becomes frustrated or tired, his or her brain and bodily control signals can change dramatically. As a result the access technology misinterprets the user’s intention, further exacerbating the decline in ability to communicate. PROPOSED RESEARCH Through this NSERC discovery grant, we will build on recent research by our lab and others, showing that certain dispositions of the user, such as anxiety, intense positive or negative emotions and mental fatigue, can be reliably detected by machine. In particular, the aim of this next phase of the applicant’s research program is to systematically and comprehensively characterize fatigue, frustration and attention, user states that are most pertinent to access technology use. To realize this objective, we will measure brain activity (electrical and blood oxygenation) and temperature distributions in the face while users repeatedly perform a number of activities designed to induce the targeted changes in user state. We select the above measurements because literature has shown that brain and facial temperature modalities are highly sensitive to a user’s emotional response. We will develop models of physiological manifestations of user state using innovative imaging and multivariate pattern discovery techniques, among other methods. Some of the questions we aim to answer will include: (1) What overall patterns of physiological change are most predictive of changes in user state? (2) Are there sequences of physiological changes across measurement modalities which could be helpful in deciphering user states as they evolve? (3) What method of signal/image characteristics and accompanying classification algorithm can discriminate user states in an online fashion? Once we have a mechanism to discern user states, we will investigate ways in which this information could be used to improve communication via an alternative access technology. Specifically, we will start by exploring ways in which knowledge of user state might guide the adaptation of the user interface, for example, by providing helpful prompts to the user. We will also consider ways in which user state might inform the continuous improvement of a machine’s understanding of the user’s control signals. IMPORTANCE This research is important because: (1) it will yield new analytical methods for the treatment and machine interpretation of multiple physiological measurements; and (2) it stands to vastly improve communication not only for children and youth, but for individuals of all ages who use technology to enable communication. The proposed program will thus strengthen Canada’s international leadership in rehabilitation engineering, in partnership with local and international academic, clinical and industrial stakeholders. ANTICIPATED OUTCOMES Through this NSERC funding, 10 applied science and engineering doctoral students and 4 post-doctoral fellows will be trained in advanced instrumentation, signal processing and signal classification techniques relating to rehabilitation engineering. These individuals will be well-positioned to take leading roles in academia and industry, and thereby further advance scientific knowledge about brain and body-machine interfaces.

挑战 尽管出现了许多可替代使用计算机和通信设备的技术，但数以万计的加拿大严重残疾儿童和青年仍然没有通信手段。这些所谓的接入技术涉及从肌肉激活到脑电波的一切。然而，现有技术的不足在一定程度上是因为它们不知道用户的情绪或精神状态。例如，当用户变得沮丧或疲倦时，他或她的大脑和身体控制信号可能会发生巨大变化。结果，接入技术曲解了用户的意图，进一步加剧了沟通能力的下降。 建议的研究 通过NSERC的这项发现拨款，我们将建立在我们的实验室和其他人最近的研究基础上，表明机器可以可靠地检测到用户的某些倾向，如焦虑、强烈的积极或消极情绪和精神疲劳。特别是，申请者下一阶段研究计划的目标是系统和全面地描述疲劳、沮丧和注意力的特征，即与获取技术使用最相关的用户状态。为了实现这一目标，我们将测量大脑活动(电和血液氧合)和面部温度分布，同时用户重复执行一些旨在诱导用户状态有针对性变化的活动。我们之所以选择上述测量，是因为文献表明，大脑和面部的温度模式对用户的情绪反应高度敏感。我们将使用创新的成像和多变量模式发现技术等方法来开发用户状态的生理表现模型。我们要回答的一些问题包括：(1)什么整体的生理变化模式最能预测用户状态的变化？(2)有没有跨测量模式的生理变化序列可以帮助破译用户状态的演变？(3)什么信号/图像特征方法和相应的分类算法可以在线方式区分用户状态？ 一旦我们有了识别用户状态的机制，我们将研究如何利用这些信息通过替代访问技术来改善通信。具体地说，我们将从探索用户状态知识可能指导用户界面调整的方法开始，例如，通过向用户提供有用的提示。我们还将考虑如何通过用户状态通知机器对用户控制信号的理解的持续改进。 重要性 这项研究之所以重要，是因为：(1)它将为多种生理测量的处理和机器解释提供新的分析方法；(2)它不仅将极大地改善儿童和青少年的沟通，而且将极大地改善使用技术使沟通成为可能的所有年龄段的个人。因此，拟议的计划将加强加拿大在康复工程方面的国际领导地位，与当地和国际学术、临床和工业利益攸关方合作。 预期结果 通过NSERC的这笔资金，10名应用科学和工程学博士生和4名博士后研究员将接受与康复工程有关的先进仪器、信号处理和信号分类技术方面的培训。这些人将处于有利地位，在学术界和工业界担任领导角色，从而进一步促进关于大脑和身体-机器接口的科学知识。