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

HCC：小：了解受损的肌肉活动以改善儿科假肢的人机界面

基本信息

批准号：
2133879
负责人：
Jonathon Schofield
金额：
$ 50万
依托单位：
University of California-Davis
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133879&HistoricalAwards=false
关键词：
HCC Small Understanding Impaired Muscle

项目摘要

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.

患有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无法移动整只手。因此，虽然许多假肢是通过测量这些肌肉的活动来操作的，但儿童能够有目的地控制受影响的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了解，这也是原因之一尽管许多“类手”系统可供成人使用，但先进的机器人假肢尚未广泛用于儿童。这项研究将调查先天上肢缺陷的儿童控制受影响肌肉的能力，然后利用这些信息开发人工智能算法，以识别孩子希望用缺失的手实现的动作。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、性别和学习等相关因素制作更有用的假肢。项目成果将包括数据集、算法以及对先天性上肢缺陷儿童能力的更深入了解，这最终将帮助医疗专业人员决定假肢治疗方案，还将带来其他儿童机器人设备的控制技术，例如外骨骼。该项目将支持每年参与为期多天的夏令营计划，该计划旨在帮助上肢缺陷的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：声波描记术，它使用小型超声波传感器、图像处理和机器学习，根据受影响的肌肉变形来推断用户预期的失手动作；肌电图 (sEMG) 模式识别，利用机器学习通过多个传感器测量受影响肌肉的电活动来推断用户预期的失手动作。首先将使用超声成像和表面肌电图测量来表征 5-17 岁儿童控制受影响肌肉的能力。然后将对这些数据进行事后分析，以微调机器学习算法，从超声成像和表面肌电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会话中使用这些系统进行视频游戏活动时，将描述声肌图和表面肌电图模式识别的实时性能以及参与者的学习效果。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持：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。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Assessing motor control capabilities in children with congenital upper limb deficiencies

评估先天性上肢缺陷儿童的运动控制能力

DOI：
发表时间：
2023
期刊：
Neural Control of Movement Annual Meeting
影响因子：
0
作者：
Fitzgerald, J;Bagley, A;Schofield, J S;James, M A;Joiner W M
通讯作者：
Joiner W M

Development and Characterization of a Multiarticulate Pediatric Hand as a Research Platform for Functional Improvements

多关节儿科手的开发和表征作为功能改善的研究平台

DOI：
发表时间：
2022
期刊：
2022 Myoelectric Control Symposium (MEC
影响因子：
0
作者：
Battraw, Marcus A;Young, Peyton R;Schofield, Jonathon S
通讯作者：
Schofield, Jonathon S

Understanding the Capacity for Children with Congenital Upper Limb Deficiency to Actuate their Affected Muscles

了解先天性上肢缺陷儿童驱动受影响肌肉的能力

DOI：
发表时间：
2023
期刊：
2023 Association of Children's Prosthetic-Orthotic Clinics- Annual Meeting
影响因子：
0
作者：
Schofield, J S;Battraw, M A;Fitzgerald, J;Joiner, W M;James, M A;Bagley, A
通讯作者：
Bagley, A

Assessing Hand Grasp Representations in Children with Congenital Upper Limb Deficiencies

评估先天性上肢缺陷儿童的手部表征

DOI：
发表时间：
2023
期刊：
Neural Control of Movement Annual Meeting
影响因子：
0
作者：
Battraw, M A;Fitzgerald, J;James, M A;Bagley, A;Joiner, W M;Schofield, J S
通讯作者：
Schofield, J S

A review of upper limb pediatric prostheses and perspectives on future advancements

儿科上肢假肢回顾及未来发展展望

DOI：
10.1097/pxr.0000000000000094
发表时间：
2022
期刊：
Prosthetics & Orthotics International
影响因子：
1.5
作者：
Battraw, Marcus A.;Fitzgerald, Justin;Joiner, Wilsaan M.;James, Michelle A.;Bagley, Anita M.;Schofield, Jonathon S.
通讯作者：
Schofield, Jonathon S.