训练运动速度对肌电控制性能有显著影响。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Troy N. Tully;Amelia E. Nelson;Jacob A. George
{"title":"训练运动速度对肌电控制性能有显著影响。","authors":"Troy N. Tully;Amelia E. Nelson;Jacob A. George","doi":"10.1109/TNSRE.2025.3610352","DOIUrl":null,"url":null,"abstract":"Our native hands are uniquely capable of operating across a wide range of speeds and forces. In contrast, most commercial myoelectric prostheses typically provide limited speed and force output. One approach to endow myoelectric prostheses with variable speed and/or force output is to use continuous kinematic positions of the prosthesis based on electromyography (EMG). Within the field of machine learning, it is well established that homogeneous training data can lead to bias that negatively impacts the run-time performance of the algorithm. Yet, most continuous decoders are trained on a homogeneous dataset involving only a single kinematic speed. To this end, we systematically investigated how different training speeds influence myoelectric control with two common continuous decoders on multiple performance metrics. We compared a Kalman filter (KF) and Convolutional Long Short-Term Memory (C-LSTM) neural network trained on slow, medium, fast, and mixed-speed datasets, evaluating their performance in offline analyses and in two real-time online tasks with the user actively in the loop. We found that training speed significantly affected algorithm performance, but effects were often algorithm dependent. Linear algorithms, like the KF, are likely to exhibit lower unintended movement errors and smoother control when trained on slow-speed data but will also struggle to generalize to higher movement speeds. In contrast, nonlinear algorithms like the C-LSTM can likely provide greater adaptability, with mixed-speed training leading to improved accuracy and task success rates across conditions. Although an often-overlooked implicit parameter, these findings explicitly demonstrate that a lack of diverse training speeds in existing myoelectric control training paradigms leads to worse decoder performance. By incorporating a range of movement speeds into training protocols or decoder design, myoelectric continuous decoders could achieve more dexterous and robust control, potentially improving prosthetic usability and retention.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3784-3792"},"PeriodicalIF":5.2000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165468","citationCount":"0","resultStr":"{\"title\":\"Training Movement Velocity Significantly Affects the Performance of Myoelectric Control\",\"authors\":\"Troy N. Tully;Amelia E. Nelson;Jacob A. George\",\"doi\":\"10.1109/TNSRE.2025.3610352\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Our native hands are uniquely capable of operating across a wide range of speeds and forces. In contrast, most commercial myoelectric prostheses typically provide limited speed and force output. One approach to endow myoelectric prostheses with variable speed and/or force output is to use continuous kinematic positions of the prosthesis based on electromyography (EMG). Within the field of machine learning, it is well established that homogeneous training data can lead to bias that negatively impacts the run-time performance of the algorithm. Yet, most continuous decoders are trained on a homogeneous dataset involving only a single kinematic speed. To this end, we systematically investigated how different training speeds influence myoelectric control with two common continuous decoders on multiple performance metrics. We compared a Kalman filter (KF) and Convolutional Long Short-Term Memory (C-LSTM) neural network trained on slow, medium, fast, and mixed-speed datasets, evaluating their performance in offline analyses and in two real-time online tasks with the user actively in the loop. We found that training speed significantly affected algorithm performance, but effects were often algorithm dependent. Linear algorithms, like the KF, are likely to exhibit lower unintended movement errors and smoother control when trained on slow-speed data but will also struggle to generalize to higher movement speeds. In contrast, nonlinear algorithms like the C-LSTM can likely provide greater adaptability, with mixed-speed training leading to improved accuracy and task success rates across conditions. Although an often-overlooked implicit parameter, these findings explicitly demonstrate that a lack of diverse training speeds in existing myoelectric control training paradigms leads to worse decoder performance. By incorporating a range of movement speeds into training protocols or decoder design, myoelectric continuous decoders could achieve more dexterous and robust control, potentially improving prosthetic usability and retention.\",\"PeriodicalId\":13419,\"journal\":{\"name\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"volume\":\"33 \",\"pages\":\"3784-3792\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165468\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11165468/\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11165468/","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

我们天生的双手具有独特的能力,可以在各种速度和力量下操作。相比之下,大多数商用肌电假肢通常提供有限的速度和力输出。一种赋予肌电假体可变速度和/或力输出的方法是基于肌电图(EMG)使用假体的连续运动学位置。在机器学习领域,同构训练数据可能导致偏差,从而对算法的运行时性能产生负面影响。然而,大多数连续解码器都是在只涉及单一运动速度的同构数据集上训练的。为此,我们系统地研究了不同的训练速度对两种常见连续解码器在多个性能指标上的肌电控制的影响。我们比较了卡尔曼滤波(KF)和卷积长短期记忆(C-LSTM)神经网络在慢速、中速、快速和混合速度数据集上的训练,评估了它们在离线分析和两个实时在线任务中的性能,其中用户主动处于循环中。我们发现训练速度显著影响算法性能,但效果往往与算法相关。像KF这样的线性算法,在低速数据上训练时,可能会表现出更低的意外运动误差和更平滑的控制,但也很难推广到更高的运动速度。相比之下,像C-LSTM这样的非线性算法可能提供更大的适应性,混合速度训练可以提高各种条件下的准确性和任务成功率。尽管这是一个经常被忽视的隐含参数,但这些研究结果明确表明,在现有的肌电控制训练范式中,缺乏不同的训练速度会导致解码器性能变差。通过将一系列运动速度纳入训练方案或解码器设计,肌电连续解码器可以实现更灵活和强大的控制,潜在地提高假肢的可用性和保留度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Training Movement Velocity Significantly Affects the Performance of Myoelectric Control
Our native hands are uniquely capable of operating across a wide range of speeds and forces. In contrast, most commercial myoelectric prostheses typically provide limited speed and force output. One approach to endow myoelectric prostheses with variable speed and/or force output is to use continuous kinematic positions of the prosthesis based on electromyography (EMG). Within the field of machine learning, it is well established that homogeneous training data can lead to bias that negatively impacts the run-time performance of the algorithm. Yet, most continuous decoders are trained on a homogeneous dataset involving only a single kinematic speed. To this end, we systematically investigated how different training speeds influence myoelectric control with two common continuous decoders on multiple performance metrics. We compared a Kalman filter (KF) and Convolutional Long Short-Term Memory (C-LSTM) neural network trained on slow, medium, fast, and mixed-speed datasets, evaluating their performance in offline analyses and in two real-time online tasks with the user actively in the loop. We found that training speed significantly affected algorithm performance, but effects were often algorithm dependent. Linear algorithms, like the KF, are likely to exhibit lower unintended movement errors and smoother control when trained on slow-speed data but will also struggle to generalize to higher movement speeds. In contrast, nonlinear algorithms like the C-LSTM can likely provide greater adaptability, with mixed-speed training leading to improved accuracy and task success rates across conditions. Although an often-overlooked implicit parameter, these findings explicitly demonstrate that a lack of diverse training speeds in existing myoelectric control training paradigms leads to worse decoder performance. By incorporating a range of movement speeds into training protocols or decoder design, myoelectric continuous decoders could achieve more dexterous and robust control, potentially improving prosthetic usability and retention.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信