HCC: Small: Understanding Impaired Muscle Activity to Improve Human-Technology Interfaces for Pediatric Prostheses

天生上肢缺陷的儿童在操作假肢时面临着几个独特的挑战，例如，他们受影响的肌肉永远无法移动完整的手。因此，虽然许多假肢是通过测量这些肌肉的活动来操作的，但儿童可以有目的地控制受影响肌肉的程度，或者如何最好地测量这些肌肉的活动以进行有效的假肢手术，仍然没有完全了解，这是先进的机器人假肢没有广泛用于儿童的原因之一，即使许多“手”系统可供成人使用。这项研究将调查先天上肢缺陷的儿童如何控制受影响的肌肉，然后将利用这些信息开发人工智能算法，以识别孩子希望用他们失去的手实现的动作。长期目标是更好地了解这些儿童的能力，以便能够根据年龄、性别和学习情况等相关因素量身定制更有用的假肢。项目成果将包括数据集、算法和对先天上肢缺陷儿童能力的更深入了解，这将最终帮助医疗专业人员决定假肢治疗方案，并将导致其他儿童机器人设备（如外骨骼）的控制技术。此外，该项目还将支持一个为期数天的夏令营项目，该项目旨在帮助上肢缺陷的儿童了解他们的能力，这将产生更广泛的影响。这个项目将通过测量皮肤表面下的肌肉运动和这些肌肉的电活动来捕捉儿童受影响肢体的肌肉活动。两种人机界面将被采用：声纳术，它使用一个小型超声波传感器，图像处理和机器学习，从受影响的肌肉变形中推断用户的手部动作；以及肌电图（sEMG）模式识别，它利用机器学习从测量受影响肌肉的电活动的多个传感器中推断出用户想要失去的手的动作。5-17岁儿童控制其受影响肌肉的能力将首先通过超声成像和肌电图测量来表征。然后对这些数据进行事后分析，以微调机器学习算法，从超声成像和表面肌电信号中提取可分类的缺失手部运动数据。最后，声纳图和表面肌电信号模式识别的实时表现以及参与者的学习效果将被表征，因为受试者在多个测试环节中使用这些系统进行视频游戏活动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Children born with upper limb deficiencies face several unique challenges when operating prosthetic limbs, for example their affected muscles will never have moved a complete hand. So, while many prosthetic limbs are operated by measuring activity in these muscles, the degree to which children can purposefully control the affected muscles or how best to measure this muscle activity for effective prosthetic operation is still not fully understood, which is one of the reasons advanced robotic prosthetic limbs are not widely available for children even though many "hand-like" systems are available for adults. This research will investigate how well children born with upper limb deficiencies can control their affected muscles, and will then use that information to develop AI algorithms to recognize the movements a child wishes to achieve with their missing hand. The long-term goal is to better understand the capabilities of these children so as to enable creation of more helpful prosthetic limbs that are tailored to relevant factors such as age, gender, and learning. Project outcomes will include datasets, algorithms, and a deeper understanding of the capabilities of children born with upper limb deficiencies, which will ultimately help medical professionals decide on prosthetic treatment options and will also lead to control techniques for other robotic devices for children, such as exoskeletons. Additional broad impact will derive from the fact that this project will support annual involvement in a multi-day summer camp program designed to help children with upper limb deficiencies learn about their capabilities. This project will capture muscle activity in children's affected limbs by measuring muscle movements below the skin's surface and the electrical activity of these same muscles. Two human-technology interfaces will be employed: sonomyography, which uses a small ultrasound sensor, image processing, and machine learning to infer the user's intended missing-hand movements from the affected muscle deformations; and electromyography (sEMG) pattern recognition, which uses machine learning to infer the user's intended missing-hand movements from multiple sensors measuring the electrical activity of the affected muscles. The capacity of children ages 5-17yrs to control their affected muscles will first be characterized using ultrasound imaging and sEMG measures. Post-hoc analyses of this data will then be performed to fine-tune machine learning algorithms that extract classifiable missing hand movement data from the ultrasound imaging and sEMG signals. Finally, the real-time performance of sonomyography and sEMG pattern recognition will be characterized as well as participant learning effects, as subjects perform videogame activities with these systems across multiple testing sessions.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.