Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Wessling, Germany.
Assistive Intelligent Robotics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
J Neural Eng. 2023 Nov 24;20(6). doi: 10.1088/1741-2552/ad0754.
Unsupervised myocontrol methods aim to create control models for myoelectric prostheses while avoiding the complications of acquiring reliable, regular, and sufficient labeled training data. A limitation of current unsupervised methods is that they fix the number of controlled prosthetic functions a priori, thus requiring an initial assessment of the user's motor skills and neglecting the development of novel motor skills over time.We developed a progressive unsupervised myocontrol (PUM) paradigm in which the user and the control model coadaptively identify distinct muscle synergies, which are then used to control arbitrarily associated myocontrol functions, each corresponding to a hand or wrist movement. The interaction starts with learning a single function and the user may request additional functions after mastering the available ones, which aligns the evolution of their motor skills with an increment in system complexity. We conducted a multi-session user study to evaluate PUM and compare it against a state-of-the-art non-progressive unsupervised alternative. Two participants with congenital upper-limb differences tested PUM, while ten non-disabled control participants tested either PUM or the non-progressive baseline. All participants engaged in myoelectric control of a virtual hand and wrist.PUM enabled autonomous learning of three myocontrol functions for participants with limb differences, and of all four available functions for non-disabled subjects, using both existing or newly identified muscle synergies. Participants with limb differences achieved similar success rates to non-disabled ones on myocontrol tests, but faced greater difficulties in internalizing new motor skills and exhibited slightly inferior movement quality. The performance was comparable with either PUM or the non-progressive baseline for the group of non-disabled participants.The PUM paradigm enables users to autonomously learn to operate the myocontrol system, adapts to the users' varied preexisting motor skills, and supports the further development of those skills throughout practice.
无监督肌电控制方法旨在为肌电假肢创建控制模型,同时避免获取可靠、规则和充足的标记训练数据的复杂性。当前无监督方法的一个限制是,它们预先确定要控制的假肢功能数量,因此需要对用户的运动技能进行初步评估,并忽略随着时间的推移新运动技能的发展。我们开发了一种渐进式无监督肌电控制 (PUM) 范式,用户和控制模型共同自适应地识别不同的肌肉协同作用,然后将这些协同作用用于控制任意关联的肌电控制功能,每个功能对应于手部或手腕运动。交互从学习单个功能开始,用户可以在掌握可用功能后请求其他功能,这使得他们的运动技能发展与系统复杂性的增加保持一致。我们进行了多次用户研究来评估 PUM,并将其与最先进的非渐进式无监督替代方案进行比较。两名上肢先天性差异的参与者测试了 PUM,而 10 名非残疾对照参与者测试了 PUM 或非渐进式基线。所有参与者都使用肌电控制虚拟手和手腕。
PUM 使具有肢体差异的参与者能够自主学习三个肌电控制功能,使非残疾参与者能够使用现有的或新识别的肌肉协同作用学习所有四个可用功能。具有肢体差异的参与者在肌电控制测试中取得了与非残疾参与者相似的成功率,但在内化新运动技能方面面临更大的困难,运动质量略逊一筹。对于非残疾参与者组,PUM 的性能与 PUM 或非渐进式基线相当。
PUM 范式使用户能够自主学习操作肌电控制系统,适应用户不同的现有运动技能,并在练习过程中支持这些技能的进一步发展。