Novel sensors for adaptive neurorehabilitation systems

用于自适应神经康复系统的新型传感器

• 批准号: RGPIN-2014-05498
• 负责人: Zariffa, José
• 金额: $ 2.19万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636996
• 关键词: Novel; sensors; adaptive; neurorehabilitation; systems

My research is concerned with developing technology that can help restore function after neurological injuries, such as spinal cord injury (SCI) or stroke. My current focus is on innovative signal and image processing solutions for monitoring and assessing the activity of the nervous system. These measures are needed as feedback control signals to create neurorehabilitation systems that can adapt to changing conditions both in the user’s body and in the external environment. In particular, implanted systems have used functional electrical stimulation (FES) of motor neural pathways to restore movement, but these devices currently rely on pre-defined patterns of stimulation and thus can only produce crude, fixed movements. The objective of the research program proposed here is to overcome this limitation by developing sensors that can provide the feedback signals needed to create closed-loop implanted FES systems. The intact nervous system achieves effective motor control only by relying on a combination of proprioceptive (limb position), tactile and visual information. My specific aims are therefore:1) To develop an implantable neural interface capable of monitoring the sensory information in peripheral nerves, such as tactile signals and limb position information. This interface will be based on multi-contact nerve cuff technology (cylindrical electrodes that wrap around a nerve, with recording contacts located on their inner surface). In one study, we will optimize the design of the electrodes and the signal processing algorithms, using first a novel computer model of a peripheral nerve, later followed by in vivo validation. In a second study, we will explore a new class of signal processing algorithms designed specifically to extract information from multi-contact nerve cuff recordings by exploiting spatiotemporal relationships in the electrical activity of the nerve. These projects are the first proposed attempt to tailor a multi-contact nerve cuff specifically to the monitoring of sensory activity. These devices have not yet been used for this purpose, but are a promising avenue because they combine suitability for chronic use in humans with improved selectivity compared to single-channel cuffs. 2) To develop a wearable sensor capable of providing visual information about the user’s environment and their interactions with it. This will make it possible to incorporate visual information into the control algorithms for closed-loop neuroprostheses. We will focus here on upper limb function. Computer vision technology will be used to analyze in real-time video from an ear-mounted wearable camera recording the user’s point of view. We will explore image processing and machine learning algorithms capable of parsing the visual information in the immediate neighbourhood of the hand with the goal of detecting environmental interactions (e.g. grasp attempt, success or failure, grip type used). No previous solution has been proposed to incorporate visual information into the control of FES. Our proposed study is the first to address this gap.Significance: The proposed research will produce novel sensor technologies that will be essential if we are to develop closed-loop neuroprosthetic systems capable of restoring natural movements to individuals with neurological injuries. Innovations in the natural science and engineering will include a new class of algorithms for processing neural signals, new computer vision techniques tailored to wearable cameras, and improved computer modeling of bioelectric activity. The research will ultimately lead to improved independence and quality of life after stroke and SCI, and reduce the economic impact of these conditions on the health care system.

我的研究关注的是开发能够帮助神经损伤后恢复功能的技术，例如脊髓损伤(SCI)或中风。我目前的重点是用于监测和评估神经系统活动的创新信号和图像处理解决方案。这些措施需要作为反馈控制信号来创建神经康复系统，该系统可以适应用户身体和外部环境中不断变化的条件。特别是，植入的系统使用运动神经通路的功能性电刺激(FES)来恢复运动，但这些设备目前依赖于预定义的刺激模式，因此只能产生粗略的、固定的运动。这里提出的研究计划的目标是通过开发能够提供所需反馈信号的传感器来克服这一限制，以创建闭环式植入的FES系统。完整的神经系统只有依靠本体感觉(肢体位置)、触觉和视觉信息的组合才能实现有效的运动控制。因此，我的具体目标是：1)开发一个可植入的神经接口，能够监测周围神经中的感觉信息，如触觉信号和肢体位置信息。这种接口将基于多接触神经袖带技术(包裹在神经周围的圆柱形电极，记录触点位于其内表面)。在一项研究中，我们将首先使用一种新的周围神经计算机模型，然后进行体内验证，从而优化电极和信号处理算法的设计。在第二项研究中，我们将探索一类新的信号处理算法，该算法专门设计用于通过利用神经电活动的时空关系从多接触神经袖带记录中提取信息。这些项目是首次提出专门为监测感觉活动量身定做多接触神经袖带的尝试。这些装置还没有被用于这一目的，但是一个有希望的途径，因为它们结合了对人类长期使用的适合性和与单通道袖口相比更好的选择性。2)开发一种可穿戴传感器，能够提供有关用户环境及其与环境交互的视觉信息。这将使将视觉信息整合到闭合神经假体的控制算法中成为可能。我们将在这里重点介绍上肢功能。计算机视觉技术将被用来实时分析来自记录用户观点的耳挂式可穿戴摄像头的视频。我们将探索图像处理和机器学习算法，能够分析手部附近的视觉信息，目的是检测环境相互作用(例如，握力尝试、成功或失败、使用的握力类型)。以前还没有提出将视觉信息合并到FES控制中的解决方案。我们提出的研究是第一个解决这一差距的研究。意义：拟议的研究将产生新的传感器技术，如果我们要开发能够恢复患有神经损伤的个人的自然运动的闭环神经假体系统，这些技术将是必不可少的。自然科学和工程学的创新将包括一类处理神经信号的新算法，为可穿戴相机量身定做的新计算机视觉技术，以及改进的生物电活动计算机建模。这项研究最终将提高中风和脊髓损伤后的独立性和生活质量，并减少这些疾病对医疗保健系统的经济影响。