CAREER: Ultrasound-based Intent Modeling and Control Framework for Neurorehabilitation and Educating Children with Disabilities and High School Students

职业：基于超声的意图建模和控制框架，用于神经康复和教育残疾儿童和高中生

• 批准号: 1750748
• 负责人: Nitin Sharma
• 金额: $ 50.91万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750748&HistoricalAwards=false
• 关键词: CAREER; Ultrasound; based; Intent; Modeling

Each year, more than 17,000 people in the United States experience a spinal cord injury. Individuals suffering from incomplete spinal cord injuries may have voluntary control of their limbs, but their strength is weakened compared to able-bodied persons. Neurorehabilitation is a therapeutic approach used to recover limb function in individuals suffering from incomplete spinal cord injury; robotic or electrical stimulation devices encourage repair of the person's nervous system through repeated, assisted exercise. The amount of assistance the device provides is based upon the user's remaining muscle function. However, correctly measuring a person's voluntary effort during therapeutic exercise is a significant technical challenge. Discrepancies in the measurement can cause the robot to provide too much or too little assistance, which can slow recovery and lead to falls during robot-assisted walking. To address the technical challenge of sensing of voluntary effort, this project will integrate ultrasound imaging with electromyography-based (i.e., electrical signals) measurement of ankle muscles that govern walking. The use of ultrasound imaging will allow direct visualization and measurement of muscle activity and minimizes interference of electrical signals from neighboring muscles. Ultrasound imaging will also be investigated as an approach for optimizing electrode placement to initiate multi-plane ankle movements. The study will test the hypothesis that ultrasound imaging-based measurement of voluntary effort is more accurate than electromyography-based prediction alone. Research and education are integrated through student-led construction of ultrasound imaging and electromyography-based human-machine interaction platforms for children with special needs. The human-machine interaction platforms may accelerate learning in children with special needs and offers research opportunities for high school-age students from underrepresented groups in STEM. The PI's long-term career goal is to build a full-scale, feedforward musculoskeletal model that predicts human intent by obtaining signals from wearable ultrasound sensors attached to different limb muscles. Toward this goal, the project will derive control strategies that use ultrasound imaging to predict weakened voluntary effort of a person with incomplete spinal cord injury and provide assistance as-needed during walking with a hybrid exoskeleton. The research objectives of the proposal are to: (1) formulate an ultrasound imaging-based observer to predict voluntary effort in ankle muscles and (2) use the prediction in an ankle control strategy. An optimal ultrasound imaging-derived surrogate signal to measure voluntary effort will be determined, and a functional mapping between the surrogate signals and voluntary effort will be formulated. The stability and convergence conditions for the observer and the ankle controller will be established. The new intent prediction and control framework will be directly compared to electromyography-based prediction and control methods. Functional electrical stimulation of the ankle muscles that produce multi-plane ankle movements will also be investigated. To target the intended ankle muscle, ultrasound imaging-based feedback will be evaluated to optimize current in the stimulation electrodes. The project facilitates a breakthrough rehabilitation therapy that uses non-invasive, wearable ultrasound sensors to collect, monitor, and control muscle activity of persons with incomplete spinal cord injury.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

每年，美国有超过17，000人经历脊髓损伤。患有不完全脊髓损伤的人可能会自愿控制他们的四肢，但与健全的人相比，他们的力量会减弱。神经康复是一种用于恢复患有不完全脊髓损伤的个体的肢体功能的治疗方法;机器人或电刺激设备通过重复的辅助锻炼来促进人的神经系统的修复。设备提供的辅助量基于用户的剩余肌肉功能。然而，正确测量一个人在治疗运动期间的自愿努力是一个重大的技术挑战。测量中的不一致性可能导致机器人提供过多或过少的辅助，这可能减慢恢复并导致机器人辅助行走期间的福尔斯。为了解决自愿努力感测的技术挑战，该项目将把超声成像与基于肌电图的（即，电信号）对支配行走的踝部肌肉的测量。超声成像的使用将允许直接可视化和测量肌肉活动，并最大限度地减少来自邻近肌肉的电信号的干扰。超声成像也将作为一种优化电极放置的方法进行研究，以启动多平面踝关节运动。这项研究将检验一个假设，即基于超声成像的自愿努力的测量比单独基于肌电图的预测更准确。研究和教育通过学生主导的超声成像和肌电图为基础的人机交互平台的建设与特殊需要的儿童相结合。人机交互平台可以加速有特殊需要的儿童的学习，并为STEM中代表性不足的高中生提供研究机会。PI的长期职业目标是建立一个全尺寸的前馈肌肉骨骼模型，通过从连接到不同肢体肌肉的可穿戴超声传感器获取信号来预测人类意图。为了实现这一目标，该项目将得出控制策略，使用超声成像来预测不完全脊髓损伤患者的自主努力减弱，并在使用混合外骨骼行走期间提供所需的帮助。该提案的研究目标是：（1）制定一个基于超声成像的观察者来预测踝关节肌肉的自愿努力和（2）在踝关节控制策略中使用预测。将确定用于测量自主努力的最佳超声成像导出的替代信号，并且将制定替代信号和自主努力之间的功能映射。将建立观测器和踝关节控制器的稳定性和收敛条件。新的意图预测和控制框架将直接与基于肌电图的预测和控制方法进行比较。功能性电刺激的踝关节肌肉，产生多平面踝关节运动也将进行研究。为了靶向预期踝关节肌肉，将评估基于超声成像的反馈，以优化刺激电极中的电流。该项目促进了一项突破性的康复治疗，使用非侵入性的，可穿戴的超声波传感器来收集，监测和控制不完全脊髓损伤患者的肌肉活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model

• DOI: 10.1109/embc46164.2021.9630046
• 发表时间: 2021-11
• 期刊: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
• 作者: Qiang Zhang;Natalie Fragnito;Alison Myers;Nitin Sharma