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

先天性上肢缺陷的儿童在操作假肢时面临着一些独特的挑战，例如他们受影响的肌肉永远不会移动一只完整的手。 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如何最好地测量这种肌肉活动以进行有效的假肢操作仍然没有完全理解，这是高级机器人假肢不能广泛用于儿童的原因之一，尽管许多“手样”系统可用于成人。 这项研究将调查出生时患有上肢缺陷的儿童如何控制他们受影响的肌肉，然后将使用这些信息开发人工智能算法，以识别儿童希望用缺失的手完成的动作。 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，性别和学习等相关因素创造更有用的假肢。 项目成果将包括数据集、算法，以及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，并将为其他儿童机器人设备（如外骨骼）带来控制技术。 该项目将支持每年参加一个为期多天的夏令营计划，以帮助上肢缺陷儿童了解他们的能力，这一事实将产生额外的广泛影响。该项目将通过测量皮肤表面以下的肌肉运动和这些肌肉的电活动来捕获儿童受影响肢体的肌肉活动。 将采用两种人机界面：声肌描记术，它使用小型超声波传感器，图像处理和机器学习来从受影响的肌肉变形中推断用户的预期缺手运动;肌电图（sEMG）模式识别，它使用机器学习来从测量受影响肌肉电活动的多个传感器中推断用户的预期缺手运动。 5- 17岁儿童控制其受影响肌肉的能力将首先使用超声成像和表面肌电图测量来表征。 然后将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sEMG信号中提取可分类的缺失手部运动数据。 最后，当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时，声肌图和表面肌电信号模式识别的实时性能以及参与者的学习效果将被表征。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.