Intelligent Sensing Laboratory, School of Engineering, Newcastle University, NE1 7RU, United Kingdom.
J Neural Eng. 2018 Oct;15(5):056003. doi: 10.1088/1741-2552/aacbfe. Epub 2018 Jun 12.
The objective of this study was to compare the use of muscles appropriate for partial-hand prostheses with those typically used for complete hand devices and to determine whether differences in their underlying neural substrates translate to different levels of myoelectric control.
We developed a novel abstract myoelectric decoder based on motor learning. Three muscle pairs, namely, an intrinsic and independent, an intrinsic and synergist and finally, an extrinsic and antagonist, were tested during abstract myoelectric control. Feedback conditions probed the roles of feed-forward and feedback mechanisms.
Both performance levels and rates of improvement were significantly higher for intrinsic hand muscles relative to muscles of the forearm. Intrinsic hand muscles showed considerable improvement generalising to decoder use without visual feedback. Results indicate that visual feedback from the decoder is used for transitioning between muscle activity levels, but not for maintaining state. Both individual and group performance were found to be strongly related to motor variability.
Physiological differences inherent to the hand muscles can translate to improved prosthesis control. Our results support the use of motor learning based techniques for upper-limb myoelectric control and strongly argues for their utility in control of partial-hand prostheses. We provide evidence of myoelectric control skill acquisition and offer a formal definition for abstract decoding in the context of prosthetic control.
本研究旨在比较部分手部假肢适用肌肉与完整手部设备常用肌肉的使用情况，并确定其潜在神经基质的差异是否转化为不同水平的肌电控制。
我们开发了一种基于运动学习的新型抽象肌电解码器。在抽象肌电控制过程中，我们测试了三对肌肉，即内在且独立、内在且协同、外在且拮抗。反馈条件探测了前馈和反馈机制的作用。
与前臂肌肉相比，内在手部肌肉的性能水平和提高速度都显著更高。内在手部肌肉在没有视觉反馈的情况下，解码器的使用有了相当大的改善，具有很好的通用性。结果表明，解码器的视觉反馈用于在肌肉活动水平之间进行转换，但不用于维持状态。个体和组的表现都与运动变异性密切相关。
手部肌肉固有的生理差异可以转化为更好的假肢控制。我们的结果支持基于运动学习的上肢肌电控制技术，并强烈证明了它们在部分手部假肢控制中的实用性。我们提供了肌电控制技能习得的证据，并在假肢控制的背景下为抽象解码提供了正式定义。
