Igual Carles, Igual Jorge
Instituto de Telecomunicaciones y Aplicaciones Multimedia (ITEAM), Universitat Politècnica de València, 46022 Valencia, Spain.
Sensors (Basel). 2024 May 13;24(10):3101. doi: 10.3390/s24103101.
Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left-right, up-down and open-close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom.
在过去十年中，基于机器学习的假肢控制器利用肌电信号变得非常流行。与分类方法相比，回归方法能够以更自然的方式对预期运动进行同步和比例控制，在分类方法中，运动数量根据定义是离散的。然而，同时用于超过两个自由度的基于回归的控制器并不常见。在本文中，我们展示了在仅具有八个传感器的相对低维特征空间中，将自适应线性回归器应用于三自由度（左右、上下和开合手运动）的同步和比例控制问题。我们表明，回归器学习过程中一个通常被忽视的关键要素是训练范式。我们提出了一种闭环程序，在该程序中人类学习如何提高生成的肌电信号质量，这也有助于获得更好的控制器。我们将其应用于10名健康受试者和3名肢体缺陷受试者。结果表明，多维目标和开环训练协议的结合显著提高了性能，对于同时控制三个自由度的最复杂情况，平均完成率从53%提高到了65%。
