NRI: INT: COLLAB: Muscle Ultrasound Sensing for Intuitive Control of Robotic Leg Prostheses

NRI：INT：COLLAB：用于机器人假肢直观控制的肌肉超声传感

• 批准号: 1925371
• 负责人: Tommaso Lenzi
• 金额: $ 60.1万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925371&HistoricalAwards=false
• 关键词: NRI; INT; COLLAB; Muscle; Ultrasound

The research objective of this project is to enable volitional control over lower-limb prostheses through the integration of sonomyographic sensing - the ultrasound imaging of amputated (i.e., residual) limb muscle morphology - to control the Utah Lightweight Leg. This powered prosthetic leg is comprised of powered ankle and knee modules, and is roughly half the weight of contemporary technologies. The project team will use sonomyographic sensors in combination with mechanical sensors to infer the user's intent in anticipation of ambulation mode or joint motion, for example locomotor transitions from walking over level ground to ramps or stairs. The team will then perform human subject experiments comparing the ability of participants with transfemoral amputation to ambulate with and without various sonomyographic control algorithms enabled. If successful, the project will have positive impact on national health and welfare by improving the lives of individuals with amputation in terms of their independence and ambulation abilities, and by mitigating undesirable secondary effects of amputation such as a fear of falling and long-term joint health. Additional broader impacts of the work include enhanced undergraduate and graduate research experiences for veterans and underrepresented minorities, as well as outreach activities to K-12 students.Robotic leg prostheses can overcome the limitations of conventional passive prostheses by generating net-positive energy during the gait cycle and actively regulating joint motion. However, scientific barriers must be overcome for robotic leg prosthesis to safely and effectively operate in real-world settings. The goal of this project is to fill the knowledge gap regarding the integration of the user's volition in the control of lightweight robotic ankle and knee prostheses. The research team will measure muscle contractions of the user's residual limb using wearable ultrasound probes. Specific objectives of this project are: 1) to identify optimal design guidelines to integrate sonomyographic sensing into state-of-the-art powered knee-ankle prostheses; 2) to determine specific algorithms that best anticipate the user's intention to perform different ambulation modes in a timely, accurate, and reliable manner; and 3) to understand how to optimally combine information gathered from sonomyography and mechanical sensors to control a robotic leg prosthesis within specific ambulation modes. Algorithms will be implemented on a lightweight robotic ankle and knee prosthesis to evaluate the hypothesis that providing users with anticipatory volitional control will lead to enhanced performance in complex and uncertain environments, thereby fostering seamless integration of robotic prostheses with human users.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.

该项目的研究目标是通过整合声肌描记传感-截肢者的超声成像（即，残余）肢体肌肉形态-控制犹他州轻量级腿。这种动力假肢由动力踝关节和膝关节模块组成，重量大约是当代技术的一半。该项目团队将使用声肌传感器与机械传感器相结合，以推断用户的意图，预计amplitude模式或关节运动，例如从平地上行走到坡道或楼梯的运动过渡。然后，该团队将进行人类受试者实验，比较经股动脉截肢的参与者在启用和不启用各种声肌描记控制算法的情况下行走的能力。如果成功，该项目将对国民健康和福利产生积极影响，改善截肢者的生活，提高他们的独立性和截肢能力，并减轻截肢的不良副作用，如害怕摔倒和长期关节健康。这项工作的其他更广泛的影响包括增强退伍军人和代表性不足的少数民族的本科生和研究生研究经验，以及K-12学生的外联活动。机器人腿部假肢可以通过在步态周期中产生净正能量并主动调节关节运动来克服传统被动假肢的局限性。然而，机器人假肢必须克服科学障碍，才能在现实世界中安全有效地运行。该项目的目标是填补知识空白，在轻量级机器人踝关节和膝关节假体的控制用户的意志的整合。研究小组将使用可穿戴的超声波探头测量用户残肢的肌肉收缩。该项目的具体目标是：1）确定最佳设计指南，将声肌描记传感集成到最先进的动力膝踝关节假体中; 2）确定最佳预期用户意图的特定算法，以及时、准确和可靠的方式执行不同的Ambestry模式;以及3）理解如何最佳地联合收割机将从声肌描记术和机械传感器收集的信息组合以在特定的Ampestion模式内控制机器人假腿。算法将在一个轻量级的机器人踝关节和膝关节假体上实现，以评估为用户提供预期的意志控制将导致在复杂和不确定的环境中增强性能的假设，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评估来支持。影响审查标准。

项目成果

A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network

• DOI: 10.1109/tnsre.2023.3248647
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Neural Systems and Rehabilitation Engineering
• 影响因子: 4.9
• 作者: Joel Mendez;Rosemarie Murray;Lukas Gabert;Nicholas P. Fey;Honghai Liu;T. Lenzi

Performance of Sonomyographic and Electromyographic Sensing for Continuous Estimation of Joint Torque During Ambulation on Multiple Terrains

用于在多地形上行走期间连续估计关节扭矩的声肌图和肌电图传感性能

• DOI: 10.1109/tnsre.2021.3134189
• 发表时间: 2021
• 期刊: IEEE Transactions on Neural Systems and Rehabilitation Engineering
• 影响因子: 4.9
• 作者: Rabe, Kaitlin G.;Lenzi, Tommaso;Fey, Nicholas P.
• 通讯作者: Fey, Nicholas P.

Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks.

通过 A 模式超声检查和卷积神经网络对经股截肢患者的步行模式进行分类。

• DOI: 10.3390/s22239350
• 发表时间: 2022-12-01
• 期刊: Sensors (Basel, Switzerland)
• 作者: Murray R;Mendez J;Gabert L;Fey NP;Liu H;Lenzi T
• 通讯作者: Lenzi T

Can a powered knee-ankle prosthesis improve weight-bearing symmetry during stand-to-sit transitions in individuals with above-knee amputations?

• DOI: 10.1186/s12984-023-01177-w
• 发表时间: 2023-05-02
• 期刊: JOURNAL OF NEUROENGINEERING AND REHABILITATION
• 影响因子: 5.1
• 作者: Hunt, Grace R.;Hood, Sarah;Gabert, Lukas;Lenzi, Tommaso
• 通讯作者: Lenzi, Tommaso