由于表面肌电图(sEMG)信号不足,多功能假体的肌电控制对于高水平截肢的个体具有挑战性。一种称为靶向肌肉神经支配(TMR)的手术技术通过提供更多的sEMG控制信号,在肌电控制方面取得了令人印象深刻的改善。在这种情况下,一些sEMG信号通道在TMR后耦合,这限制了用于上肢假体的常规基于幅度的控制的性能。
在本文中,尝试了两种不同的方法(训练和算法)来解决TMR后经截肢者的问题。首先,研究了康复训练对产生独立sEMG信号的影响。结果表明,在康复训练约两个月后,记录的一些sEMG信号仍然耦合在目标肌肉上。其次,然后应用模式识别(PR)算法对sEMG信号进行分类。在第二种方式中,为了进一步提高假肢控制的实时性,使用称为基于平均绝对值(基于MAV)的阈值切换的后处理方法。
使用改进的算法,在改良的动作研究手臂试验(ARAT)的一个子集中显示出实质性的改善。与无后处理方法的普通PR控制相比,在多数投票的情况下,总分增加了18%以上,在基于MAV的阈值切换的情况下,总分增加了58%以上。截肢者能够使用基于MAV的标准阈值开关在分配的时间内完成所有任务。主观上,截肢者更喜欢使用基于MAV的阈值开关进行PR控制,并报告说它在实验和实际使用中都更准确,更平滑。
尽管对TMR患者进行康复训练后sEMG信号仍然耦合,通过应用PR控制与基于MAV的阈值开关的组合,改善了假肢手术的在线性能。
回顾注册http://www。chictr.org.cn/showproj.aspx?proj=22058。
Myoelectric control of multifunctional prostheses is challenging for individuals with high-level amputations due to insufficient surface electromyography (sEMG) signals. A surgical technique called targeted muscle reinnervation (TMR) has achieved impressive improvements in myoelectric control by providing more sEMG control signals. In this
case, some channels of sEMG signals are coupled after TMR, which limits the performance of conventional amplitude-based control for upper-limb prostheses.
In this paper, two different ways (training and algorithms) were attempted to solve the problem in a transhumeral amputee after TMR. Firstly, effect of rehabilitation training on generating independent sEMG signals was investigated. The results indicated that some sEMG signals recorded were still coupled over the targeted muscles after rehabilitation training for about two months. Secondly, pattern recognition (PR) algorithm was then applied to classify the sEMG signals. In the second way, to further improve the real-time performance of prosthetic control, a post-processing method named as mean absolute value-based (MAV-based) threshold switches was utilized.
Using the improved algorithms, substantial improvement was shown in a subset of the modified Action Research Arm Test (ARAT). Compared with common PR control without post-processing method, the total scores increased more than 18% with majority vote and more than 58% with MAV-based threshold switches. The amputee was able to finish all the tasks within the allotted time with the standard MAV-based threshold switches. Subjectively the amputee preferred the PR control with MAV-based threshold switches and reported it to be more accurate and much smoother both in experiment and practical use.
Although the sEMG signals were still coupled after rehabilitation training on the TMR patient, the online performance of the prosthetic operation was improved through application of PR control with combination of the MAV-based threshold switches.
Retrospectively registered http://www.chictr.org.cn/showproj.aspx?proj=22058 .