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

• 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

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.

在操作假肢时，出生的上肢缺陷的孩子面临着几个独特的挑战，例如，受影响的肌肉永远不会动手。 因此，尽管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的肌肉的程度，或者如何最好地测量这种肌肉活动以进行有效的假体操作，这仍然是不完全了解的，这是高级机器人假体假肢的原因之一，即使许多“手工”的“手持”系统也无法广泛使用。 这项研究将研究上肢缺陷的儿童如何控制受影响的肌肉，然后使用该信息来开发AI算法来认识孩子希望通过失踪的动作来实现的动作。 长期目标是更好地了解这些孩子的能力，以便创造出针对年龄，性别和学习等相关因素量身定制的更有帮助的假肢。 项目成果将包括数据集，算法，以及对上肢缺陷的儿童的能力的更深入的了解，最终将帮助医疗专业人员决定假肢治疗方案，并还将为其他机器人设备提供其他机器人设备的控制技术，例如外骨骨骼。 额外的广泛影响将来自以下事实：该项目将支持年度参与多日夏令营计划，旨在帮助上肢缺陷的儿童了解其能力。该项目将通过测量皮肤表面下方的肌肉运动以及这些相同肌肉的电活动来捕捉儿童受影响的肢体的肌肉活动。 将采用两个人类技术界面：使用小型超声传感器，图像处理和机器学习来推断用户从受影响的肌肉变形中推断出用户预期的缺失手动的运动；和肌电图（SEMG）模式识别，它使用机器学习来从测量受影响肌肉的电活动的多个传感器中推断出用户的缺失手机。 首先，使用超声成像和SEMG测量值对5-17岁儿童控制受影响的肌肉的能力进行表征。 然后，将对这些数据进行事后分析，然后对微调机学习算法进行，以从超声成像和SEMG信号中提取可分类的缺失移动数据。 最后，随着受试者在多个测试会议中与这些系统进行视频游戏活动，这是NSF的法定任务，并且被认为是值得通过基金会的知识分子和更广泛的影响审查综述的审查标准，这奖项反映了NSF的法定任务。

项目成果

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

• 发表时间: 2023
• 期刊: Neural Control of Movement Annual Meeting
• 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

• 发表时间: 2022
• 期刊: 2022 Myoelectric Control Symposium (MEC
• 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

• 发表时间: 2023
• 期刊: 2023 Association of Children's Prosthetic-Orthotic Clinics- Annual Meeting
• 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

• 发表时间: 2023
• 期刊: Neural Control of Movement Annual Meeting
• 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.