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杜氏肌营养不良症患者手腕/手部运动的实时肌电控制:一项病例研究。

Real-time myoelectric control of wrist/hand motion in Duchenne muscular dystrophy: A case study.

作者信息

Nizamis Kostas, Ayvaz Anıl, Rijken Noortje H M, Koopman Bart F J M, Sartori Massimo

机构信息

Systems Engineering and Multidisciplinary Design Group, Department of Design, Production, and Management, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands.

Neuromechanical Modelling and Engineering lab, Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, Enschede, Netherlands.

出版信息

Front Robot AI. 2023 Apr 6;10:1100411. doi: 10.3389/frobt.2023.1100411. eCollection 2023.

Abstract

Duchenne muscular dystrophy (DMD) is a genetic disorder that induces progressive muscular degeneration. Currently, the increase in DMD individuals' life expectancy is not being matched by an increase in quality of life. The functioning of the hand and wrist is central for performing daily activities and for providing a higher degree of independence. Active exoskeletons can assist this functioning but require the accurate decoding of the users' motor intention. These methods have, however, never been systematically analyzed in the context of DMD. This case study evaluated direct control (DC) and pattern recognition (PR), combined with an admittance model. This enabled customization of myoelectric controllers to one DMD individual and to a control population of ten healthy participants during a target-reaching task in 1- and 2- degrees of freedom (DOF). We quantified real-time myocontrol performance using target reaching times and compared the differences between the healthy individuals and the DMD individual. Our findings suggest that despite the muscle tissue degeneration, the myocontrol performance of the DMD individual was comparable to that of the healthy individuals in both DOFs and with both control approaches. It was also evident that PR control performed better for the 2-DOF tasks for both DMD and healthy participants, while DC performed better for the 1-DOF tasks. The insights gained from this study can lead to further developments for the intuitive multi-DOF myoelectric control of active hand exoskeletons for individuals with DMD.

摘要

杜兴氏肌肉营养不良症(DMD)是一种导致进行性肌肉退化的遗传性疾病。目前,DMD患者预期寿命的增加并未伴随着生活质量的提高。手和腕部的功能对于进行日常活动以及提供更高程度的独立性至关重要。主动式外骨骼可以辅助这种功能,但需要准确解码用户的运动意图。然而,这些方法从未在DMD的背景下进行过系统分析。本案例研究评估了直接控制(DC)和模式识别(PR),并结合了导纳模型。这使得肌电控制器能够针对一名DMD患者和十名健康参与者的对照组进行定制,用于在1自由度和2自由度(DOF)的目标达成任务中。我们使用目标达成时间来量化实时肌电控制性能,并比较健康个体和DMD患者之间的差异。我们的研究结果表明,尽管存在肌肉组织退化,但DMD患者在两个自由度以及两种控制方法下的肌电控制性能与健康个体相当。同样明显的是,对于DMD患者和健康参与者,PR控制在2自由度任务中表现更好,而DC在1自由度任务中表现更好。从这项研究中获得的见解可以为DMD患者主动式手部外骨骼的直观多自由度肌电控制带来进一步的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46c7/10116050/a10f64b21304/frobt-10-1100411-g001.jpg

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