Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees.
Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees.
复制标题
旨在减轻手臂位置对经桡侧截肢者基于肌电图模式识别的运动分类的影响。
DOI：
10.1186/1743-0003-9-74
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发表时间：
2012-10-05
影响因子：
5.1
通讯作者：
Li G
中科院分区：
文献类型：
--
作者：
Geng Y;Zhou P;Li G
基于肌电模式识别的多功能肌电假体系统的控制策略通常在受控实验室环境中进行研究。在这些肌电假体系统在临床上可行之前，有必要评估理想的实验室环境和实际使用之间的一些差异对控制性能的影响。一个重要的障碍是手臂位置变化的影响，当在不同的手臂位置执行相同的动作时，会导致肌电模式的变化。本研究旨在探讨手臂位置变化对上肢截肢者基于肌电模式识别的运动分类的影响及减少这些影响的解决方案。对5名单侧横径截肢者，同时采集截肢和完整手臂的肌电信号和三轴加速度计机械肌肉成像(ACC-MMG)信号，分别在研究中考虑的5个手臂位置进行6类手臂和手的运动。根据运动分类误差来评估手臂位置改变的效果，并比较截肢和完整手臂之间的差异。然后对提出的三种方法在减弱手臂位置影响方面的性能进行了评估。使用肌电信号，截肢的5个手臂位置和5个受试者的平均位置内和位置间分类错误分别约为7.3%和29.9%，比完整手臂的位置内和位置间分类错误分别低1.0%和10%。ACC-MMG信号产生的位置内分类错误与EMG相似(9.9%)，但它们的位置间分类错误要高得多，平均为手臂位置和受试者81.1%。当训练集包含所有五个手臂位置的肌电数据时，截肢手臂的平均分类错误率达到10.8%左右。使用两级级联分类器，在所有五个手臂位置上的平均分类误差约为9.0%。将ACC-MMG通道从8个减少到2个，在截肢手臂中，所有五个手臂位置的平均位置分类误差从0.7%增加到1.0%。基于肌电模式识别的运动分类方法的性能很大程度上依赖于手臂位置。这种依赖在完整手臂中比在截肢手臂中略强，这表明与肌电假体的实际应用相关的研究应该以肢体截肢者为受试者，而不是使用有能力的身体受试者。将ACC-MMG用于肢体位置识别，肌电用于肢体运动分类的两级级联分类器模式可能是一种很有前途的方法，可以减少肢体位置变化对分类性能的影响。
Electromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Before these myoelectric prosthesis systems are clinically viable, it will be necessary to assess the effect of some disparities between the ideal laboratory setting and practical use on the control performance. One important obstacle is the impact of arm position variation that causes the changes of EMG pattern when performing identical motions in different arm positions. This study aimed to investigate the impacts of arm position variation on EMG pattern-recognition based motion classification in upper-limb amputees and the solutions for reducing these impacts. With five unilateral transradial (TR) amputees, the EMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were simultaneously collected from both amputated and intact arms when performing six classes of arm and hand movements in each of five arm positions that were considered in the study. The effect of the arm position changes was estimated in terms of motion classification error and compared between amputated and intact arms. Then the performance of three proposed methods in attenuating the impact of arm positions was evaluated. With EMG signals, the average intra-position and inter-position classification errors across all five arm positions and five subjects were around 7.3% and 29.9% from amputated arms, respectively, about 1.0% and 10% low in comparison with those from intact arms. While ACC-MMG signals could yield a similar intra-position classification error (9.9%) as EMG, they had much higher inter-position classification error with an average value of 81.1% over the arm positions and the subjects. When the EMG data from all five arm positions were involved in the training set, the average classification error reached a value of around 10.8% for amputated arms. Using a two-stage cascade classifier, the average classification error was around 9.0% over all five arm positions. Reducing ACC-MMG channels from 8 to 2 only increased the average position classification error across all five arm positions from 0.7% to 1.0% in amputated arms. The performance of EMG pattern-recognition based method in classifying movements strongly depends on arm positions. This dependency is a little stronger in intact arm than in amputated arm, which suggests that the investigations associated with practical use of a myoelectric prosthesis should use the limb amputees as subjects instead of using able-body subjects. The two-stage cascade classifier mode with ACC-MMG for limb position identification and EMG for limb motion classification may be a promising way to reduce the effect of limb position variation on classification performance.
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DOI：
10.1109/tbme.2011.2159216
发表时间：
2011-09
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Young AJ;Hargrove LJ;Kuiken TA
通讯作者：
Kuiken TA
影响因子：
2.2
作者：
Engelhart, K;Hudgins, B;Stevenson, M
通讯作者：
Stevenson, M
DOI：
10.1016/j.apmr.2005.03.034
发表时间：
2005-10-01
影响因子：
4.3
作者：
Silva, J;Heim, W;Chau, T
通讯作者：
Chau, T
影响因子：
120.7
作者：
Kuiken, Todd A.;Li, Guanglin;Lock, Blair A.;Lipschutz, Robert D.;Miller, Laura A.;Stubblefield, Kathy A.;Englehart, Kevin B.
通讯作者：
Englehart, Kevin B.
DOI：
10.1109/tnsre.2007.908376
发表时间：
2007-12-01
影响因子：
4.9
作者：
Momen, Kaveh;Krishnan, Sridhar;Chau, Tom
通讯作者：
Chau, Tom