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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=669578
• 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.

我的研究涉及开发技术，可以帮助恢复神经损伤后的功能，如脊髓损伤（SCI）或中风。 我目前的重点是创新的信号和图像处理解决方案，用于监测和评估神经系统的活动。 需要这些措施作为反馈控制信号，以创建可以适应用户身体和外部环境中变化的条件的神经康复系统。 特别是，植入系统已经使用运动神经通路的功能性电刺激（FES）来恢复运动，但是这些设备目前依赖于预定义的刺激模式，因此只能产生粗糙的固定运动。 这里提出的研究计划的目标是克服这种限制，通过开发传感器，可以提供所需的反馈信号，以创建闭环植入FES系统。完整的神经系统只有依靠本体感受（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）开发一种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）的植入式神经接口。 该接口将基于多触点神经袖带技术（缠绕神经的圆柱形电极，记录触点位于其内表面）。 在一项研究中，我们将优化电极和信号处理算法的设计，首先使用一种新的外周神经计算机模型，然后进行体内验证。 在第二项研究中，我们将探索一类新的信号处理算法，专门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。 这些项目是第一次提出尝试定制一个多接触神经袖口专门监测感觉活动。 这些器械尚未用于此目的，但却是一种有前景的途径，因为与单通道袖带相比，它们将联合收割机长期用于人体的适用性与改善的选择性相结合。 **2）开发一种可穿戴传感器，该传感器能够提供有关用户环境及其交互的视觉信息。这将使将视觉信息纳入闭环神经假体的控制算法成为可能。 我们将重点放在上肢功能上。 计算机视觉技术将被用于实时分析来自耳戴式可穿戴摄像机的视频，记录用户的视角。 我们将探索图像处理和机器学习算法，这些算法能够解析手附近的视觉信息，目的是检测环境相互作用（例如，抓取尝试，成功或失败，使用的抓握类型）。 没有以前的解决方案已经提出了将视觉信息纳入FES的控制。我们提出的研究是第一个解决这一差距的研究。**重要性：这项拟议中的研究将产生新的传感器技术，如果我们要开发能够恢复神经损伤个体自然运动的闭环神经假体系统，这将是必不可少的。 自然科学和工程领域的创新将包括一类新的神经信号处理算法，为可穿戴相机量身定制的新计算机视觉技术，以及改进的生物电活动计算机建模。 这项研究将最终导致改善中风和SCI后的独立性和生活质量，并减少这些条件对医疗保健系统的经济影响。