IEEE Trans Neural Syst Rehabil Eng. 2018 May;26(5):1046-1055. doi: 10.1109/TNSRE.2018.2826981.
On-going developments in myoelectric prosthesis control have provided prosthesis users with an assortment of control strategies that vary in reliability and performance. Many studies have focused on improving performance by providing feedback to the user but have overlooked the effect of this feedback on internal model development, which is key to improve long-term performance. In this paper, the strength of internal models developed for two commonly used myoelectric control strategies: raw control with raw feedback (using a regression-based approach) and filtered control with filtered feedback (using a classifier-based approach), were evaluated using two psychometric measures: trial-by-trial adaptation and just-noticeable difference. The performance of both strategies was also evaluated using Schmidt's style target acquisition task. Results obtained from 24 able-bodied subjects showed that although filtered control with filtered feedback had better short-term performance in path efficiency ( ), raw control with raw feedback resulted in stronger internal model development ( ), which may lead to better long-term performance. Despite inherent noise in the control signals of the regression controller, these findings suggest that rich feedback associated with regression control may be used to improve human understanding of the myoelectric control system.
肌电假体控制的持续发展为假体使用者提供了各种控制策略，这些策略在可靠性和性能方面存在差异。许多研究都集中在通过向用户提供反馈来提高性能，但忽略了这种反馈对内部模型开发的影响，而内部模型开发是提高长期性能的关键。在本文中，使用两种心理物理测量方法评估了两种常用肌电控制策略（使用基于回归的方法的原始控制和原始反馈以及使用基于分类器的方法的滤波控制）所开发的内部模型的强度：逐次适应和刚好可察觉差异。还使用施密特风格的目标获取任务评估了两种策略的性能。来自 24 名健康受试者的结果表明，尽管使用基于分类器的方法的滤波控制和滤波反馈具有更好的短期路径效率（），但使用基于回归的方法的原始控制和原始反馈导致更强的内部模型开发（），这可能导致更好的长期性能。尽管回归控制器的控制信号中存在固有噪声，但这些发现表明，与回归控制相关的丰富反馈可能被用于提高人类对肌电控制系统的理解。
