SCH: INT: An Adaptive Robotic Hand Orthosis with Multimodal Sensing and Continuous Learning

SCH：INT：具有多模态传感和持续学习功能的自适应机器人手矫形器

• 批准号: 10222795
• 负责人: Matei Ciocarlie
• 金额: $ 28.03万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10222795
• 关键词: Activities of Daily Living; Address; Affect; Calibration; Data; Devices; Distal; Environment; Exclusion; Finger joint structure; Hand; Health; Impairment; Learning; Manuals; Methods; Modality; Motor; Movement; Muscle; Myopathy; Orthotic Devices; Outcome; Population; Prevalence; Rehabilitation device; Rehabilitation therapy; Research; Robotics; Role; Self-Help Devices; Sensory; Stroke; Supervision; Testing; Training; Upper Extremity; arm; base; design; hand dysfunction; improved; innovation; learning progression; multimodal data; multimodality; novel strategies; post stroke; pressure; robot exoskeleton; robotic device; spasticity; stroke patient; stroke survivor; synergism

We propose to develop a wearable robotic hand orthosis, able to act as a multimodal sensory platform and to physically assist functional hand movement. Working with stroke patients, we will deploy this orthosis for extended use in retraining the upper limb to improve the ability to perform activities of daily living in a simulated domestic environment. In these sessions, we will test our platform in two possible roles: an active orthosis as well as a rehabilitation device. To enable extended use of the orthosis for functional tasks, we rely on two novel approaches. The first is that of a robotic hand orthosis seen as a platform for both actuation and multimodal sensing. This will be the first device to collect multimodal data on the assisted impaired hand during functional tasks, combining information such as applied motor force, finger joint angles, fingertip pose, applied contact pressure, muscle activity, etc. Based on this data, we propose a multimodal learning mechanism for inferring user intent in order to operate the device. As a significant innovation, this approach lends itself to continuous learning: different sensing modalities can inform and train each other during use, in the absence of manually provided ground truth. Thus, we aim to remove a longstanding challenge for user-controlled assistive devices: the need for repeated calibration and supervision. The second novel approach is that of an assistive platform used to characterize and develop mitigation strategies for unwanted and abnormal muscle activations, an important and currently unaddressed challenge for achieving functional movement of the combined proximal and distal upper limb. These phenomena, which directly oppose training movements, have generally been ignored in the design of robotic devices, or addressed through blunt methods such as a general increase of the applied force in order to overcome spasticity, or exclusion of the proximal upper limb from the task in order to avoid synergies. Our sensing capabilities will allow us to study how applied assistive forces affects these phenomena, and develop targeted strategies to mitigate them.

我们建议开发一种可穿戴机器人手矫形器，能够充当多模式感觉平台 并在身体上协助手部功能性运动。与中风患者合作，我们将部署此 长期使用的矫形器，用于重新训练上肢，以提高日常活动的能力 生活在模拟的家庭环境中。在这些会议中，我们将在两种可能的情况下测试我们的平台 作用：主动矫形器和康复装置。 为了能够扩展使用矫形器来执行功能任务，我们依靠两种新颖的方法。第一个是 机器人手矫形器被视为驱动和多模式传感的平台。这将是 第一个在功能性任务期间收集辅助受损手的多模式数据的设备， 结合应用的电机力、指关节角度、指尖姿势、应用的接触等信息 压力、肌肉活动等。基于这些数据，我们提出了一种多模式学习机制 推断用户意图以操作设备。作为一项重大创新，这种方法适用于 持续学习：不同的传感方式可以在使用过程中相互告知和训练 缺乏手动提供的基本事实。因此，我们的目标是消除长期存在的挑战 用户控制的辅助设备：需要反复校准和监督。 第二种新颖方法是用于描述和开发缓解措施的辅助平台 针对不需要的和异常的肌肉激活的策略，这是一个重要且目前尚未解决的问题 实现上肢近端和远端组合功能运动的挑战。这些 直接反对训练动作的现象在设计中通常被忽略 机器人设备，或通过钝的方法解决，例如普遍增加施加的力 为了克服痉挛，或将上肢近端排除在任务之外，以避免 协同作用。我们的传感能力将使我们能够研究所施加的辅助力如何影响这些 现象，并制定有针对性的策略来缓解这些现象。