Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Münchner Str. 20, 82234, Weßling, Germany.
Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
J Neuroeng Rehabil. 2023 Apr 7;20(1):39. doi: 10.1186/s12984-023-01171-2.
Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the user. Nevertheless, a long-term study assessing the efficacy of incremental myocontrol is still missing, partially due to the lack of an adequate tool to do so. In this work we close this gap and report about a person with upper-limb absence who learned to control a dexterous hand prosthesis using incremental myocontrol through a novel functional assessment protocol called SATMC (Simultaneous Assessment and Training of Myoelectric Control).
The participant was fitted with a custom-made prosthetic setup with a controller based on Ridge Regression with Random Fourier Features (RR-RFF), a non-linear, incremental machine learning method, used to build and progressively update the myocontrol system. During a 13-month user study, the participant performed increasingly complex daily-living tasks, requiring fine bimanual coordination and manipulation with a multi-fingered hand prosthesis, in a realistic laboratory setup. The SATMC was used both to compose the tasks and continually assess the participant's progress. Patient satisfaction was measured using Visual Analog Scales.
Over the course of the study, the participant progressively improved his performance both objectively, e.g., the time required to complete each task became shorter, and subjectively, meaning that his satisfaction improved. The SATMC actively supported the improvement of the participant by progressively increasing the difficulty of the tasks in a structured way. In combination with the incremental RR-RFF allowing for small adjustments when required, the participant was capable of reliably using four actions of the prosthetic hand to perform all required tasks at the end of the study.
Incremental myocontrol enabled an upper-limb amputee to reliably control a dexterous hand prosthesis while providing a subjectively satisfactory experience. The SATMC can be an effective tool to this aim.
基于机器学习的假肢肌电控制由于对训练过程和日常控制的可靠性不满意，导致其放弃率很高。增量肌电控制是一种很有前途的方法，因为它允许按需更新系统，从而与用户保持持续交互。然而，由于缺乏合适的工具，仍然缺少对增量肌电控制的有效性进行长期评估的研究。在这项工作中，我们弥补了这一空白，并报告了一名上肢缺失的患者，他使用一种名为 SATMC（肌电控制的同时评估和训练）的新型功能评估协议，通过基于随机傅里叶特征的脊回归（RR-RFF）的定制假肢设置，学会控制灵巧手假肢。
参与者配备了一种定制的假肢设置，该设置的控制器基于随机傅里叶特征的脊回归（RR-RFF），这是一种非线性的、增量式机器学习方法，用于构建和逐步更新肌电控制系统。在为期 13 个月的用户研究中，参与者在一个现实的实验室环境中，执行越来越复杂的日常生活任务，需要精细的双手协调和使用多指手假肢进行操作。SATMC 用于组合任务并不断评估参与者的进展。使用视觉模拟量表来衡量患者满意度。
在研究过程中，参与者的表现逐渐提高，无论是客观上，例如，完成每项任务所需的时间变得更短，还是主观上，即他的满意度提高。SATMC 通过以结构化的方式逐步增加任务的难度，积极支持参与者的改进。结合允许在需要时进行小调整的增量 RR-RFF，参与者能够在研究结束时可靠地使用假肢手的四个动作来执行所有要求的任务。
增量肌电控制使上肢截肢者能够可靠地控制灵巧手假肢，同时提供主观上满意的体验。SATMC 可以成为实现这一目标的有效工具。
