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: 9929143
• 负责人: Matei Ciocarlie
• 金额: $ 29.04万
• 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/9929143
• 关键词: Activities of Daily Living; Address; Affect; Calibration; Data; Devices; Distal; Environment; Exclusion; Finger joint structure; Hand; Health; Impairment; Instruction; 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; 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的机器人手矫形器，能够作为一个多模式的感觉平台 并在物理上辅助功能性手部运动。与中风患者一起工作，我们将部署这个 用于再训练上肢以提高日常活动能力的延长使用的矫形器 生活在一个模拟的家庭环境中在这些会议中，我们将测试我们的平台在两个可能的 作用：主动矫形器以及康复设备。 为了使矫形器能够扩展用于功能任务，我们依赖于两种新颖的方法。一是 机器人手矫形器被视为用于致动和多模态感测的平台。这将是 第一设备在功能任务期间收集关于辅助受损手的多模态数据， 将诸如所施加的电机力、手指关节角度、指尖姿态、所施加的接触等信息 压力，肌肉活动等。基于这些数据，我们提出了一种多模态学习机制， 推断用户意图以便操作所述设备。作为一项重大创新，这种方法有助于 持续学习：不同的感测模态可以在使用期间相互通知和训练， 缺少手动提供的地面实况。因此，我们的目标是消除一个长期的挑战， 用户控制的辅助设备：需要反复校准和监督。 第二种新方法是一种用于描述和开发缓解措施的辅助平台 不必要的和异常的肌肉激活的策略，一个重要的，目前尚未解决的 实现组合的近端和远端上肢的功能性运动的挑战。这些 与训练动作直接对立的现象，在设计中往往被忽视， 机器人设备，或通过钝方法解决，如在 为了克服痉挛，或从任务中排除近端上肢，以避免 协同作用。我们的感知能力将使我们能够研究应用辅助力如何影响这些 我们必须采取行动，消除这些现象，并制定有针对性的战略来缓解这些现象。