School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK.
School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK.
Sci Rep. 2020 Oct 9;10(1):16872. doi: 10.1038/s41598-020-72574-7.
The ultimate goal of machine learning-based myoelectric control is simultaneous and independent control of multiple degrees of freedom (DOFs), including wrist and digit artificial joints. For prosthetic finger control, regression-based methods are typically used to reconstruct position/velocity trajectories from surface electromyogram (EMG) signals. Unfortunately, such methods have thus far met with limited success. In this work, we propose action decoding, a paradigm-shifting approach for independent, multi-digit movement intent prediction based on multi-output, multi-class classification. At each moment in time, our algorithm decodes movement intent for each available DOF into one of three classes: open, close, or stall (i.e., no movement). Despite using a classifier as the decoder, arbitrary hand postures are possible with our approach. We analyse a public dataset previously recorded and published by us, comprising measurements from 10 able-bodied and two transradial amputee participants. We demonstrate the feasibility of using our proposed action decoding paradigm to predict movement action for all five digits as well as rotation of the thumb. We perform a systematic offline analysis by investigating the effect of various algorithmic parameters on decoding performance, such as feature selection and choice of classification algorithm and multi-output strategy. The outcomes of the offline analysis presented in this study will be used to inform the real-time implementation of our algorithm. In the future, we will further evaluate its efficacy with real-time control experiments involving upper-limb amputees.
基于机器学习的肌电控制的最终目标是同时独立控制多个自由度（DOFs），包括手腕和手指人工关节。对于假肢手指控制，基于回归的方法通常用于从表面肌电图（EMG）信号中重建位置/速度轨迹。不幸的是，到目前为止，这些方法的效果有限。在这项工作中，我们提出了动作解码，这是一种基于多输出、多类分类的用于独立、多数字运动意图预测的范式转变方法。在每个时间点，我们的算法将每个可用自由度的运动意图解码为三个类别之一：打开、关闭或停止（即无运动）。尽管我们的方法使用分类器作为解码器，但可以实现任意的手姿势。我们分析了一个由我们之前记录和发布的公共数据集，该数据集由 10 名健全人和 2 名桡骨截肢者参与者的测量数据组成。我们证明了使用我们提出的动作解码范式来预测所有五个手指以及拇指旋转的运动动作的可行性。我们通过研究各种算法参数（如特征选择以及分类算法和多输出策略的选择）对解码性能的影响，进行了系统的离线分析。本研究中提出的离线分析结果将用于指导我们算法的实时实现。将来，我们将通过涉及上肢截肢者的实时控制实验进一步评估其功效。
