Novel sensors for adaptive neurorehabilitation systems

我的研究是关于开发技术，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。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体内和外部环境变化的条件。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，但这些设备目前依赖于预先定义的刺激模式，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通过开发能够提供创建闭环植入FES系统所需的反馈信号的传感器来克服这一限制。完整的神经系统只有依靠本体感觉（肢体位置）、触觉和视觉信息的结合才能实现有效的运动控制。因此，我的具体目标是：1)开发一种植入式神经接口，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。该接口将基于多接触神经袖技术（围绕神经的圆柱形电极，在其内表面记录接触）。在一项研究中，我们将优化电极的设计和信号处理算法，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。在第二项研究中，我们将探索一类新的信号处理算法，通过利用神经电活动的时空关系，专门设计用于从多接触神经袖记录中提取信息。这些项目是首次提出尝试定制多接触神经袖专门监测感官活动。这些装置尚未用于此目的，但由于与单通道袖口相比，它们结合了人类长期使用的适用性和更高的选择性，因此是一种很有前途的途径。2)开发一种可穿戴传感器，能够提供有关用户环境及其与之交互的视觉信息。这将使得将视觉信息纳入闭环神经假体的控制算法成为可能。我们将重点关注上肢的功能。计算机视觉技术将用于分析实时视频，从一个耳戴式可穿戴摄像头记录用户的观点。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部附近的视觉信息，目的是检测环境相互作用（例如，抓取尝试，成功或失败，使用的抓取类型）。以前没有提出过将视觉信息纳入FES控制的解决方案。我们提出的研究是第一个解决这一差距的研究。意义：这项研究将产生新的传感器技术，如果我们要开发闭环神经修复系统，能够恢复神经损伤患者的自然运动，这将是必不可少的。自然科学和工程领域的创新将包括处理神经信号的新算法，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。

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.