PDF(Pubmed)
Abstract：
Objective.Advanced myoelectric hands enable users to select from multiple functional grasps. Current methods for controlling these hands are unintuitive and require frequent recalibration. This case study assessed the performance of tasks involving grasp selection, object interaction, and dynamic postural changes using intramuscular electrodes with regenerative peripheral nerve interfaces (RPNIs) and residual muscles.Approach.One female with unilateral transradial amputation participated in a series of experiments to compare the performance of grasp selection controllers with RPNIs and intramuscular control signals with controllers using surface electrodes. These experiments included a virtual grasp-matching task with and without a concurrent cognitive task and physical tasks with a prosthesis including standardized functional assessments and a functional assessment where the individual made a cup of coffee (\'Coffee Task\') that required grasp transitions.Main results.In the virtual environment, the participant was able to select between four functional grasps with higher accuracy using the RPNI controller (92.5%) compared to surface controllers (81.9%). With the concurrent cognitive task, performance of the virtual task was more consistent with RPNI controllers (reduced accuracy by 1.1%) compared to with surface controllers (4.8%). When RPNI signals were excluded from the controller with intramuscular electromyography (i.e. residual muscles only), grasp selection accuracy decreased by up to 24%. The participant completed the Coffee Task with 11.7% longer completion time with the surface controller than with the RPNI controller. She also completed the Coffee Task with 11 fewer transition errors out of a maximum of 25 total errors when using the RPNI controller compared to surface controller.Significance.The use of RPNI signals in concert with residual muscles and intramuscular electrodes can improve grasp selection accuracy in both virtual and physical environments. This approach yielded consistent performance without recalibration needs while reducing cognitive load associated with pattern recognition for myoelectric control (clinical trial registration number NCT03260400).
摘要：
Objective.先进的肌电手使用户能够从多个功能掌握中进行选择。用于控制这些手的当前方法是不直观的并且需要频繁的重新校准。本案例研究评估了涉及抓取选择的任务的性能，对象交互，以及使用具有再生周围神经界面（RPNI）和残余肌肉的肌内电极的动态姿势变化。方法。一名单侧经桡骨截肢的女性参加了一系列实验，以比较具有RPNI的抓握选择控制器和使用表面电极的控制器的肌肉控制信号的性能。这些实验包括一个虚拟的抓握匹配任务，有和没有并发的认知任务，以及带有假肢的物理任务，包括标准化的功能评估和功能评估，其中个人制作一杯咖啡（“咖啡任务”）需要抓握过渡。主要结果。在虚拟环境中，与地面控制器(81.9%)相比,参与者能够使用RPNI控制器(92.5%)在4种功能抓握之间进行更高精度的选择.伴随着并发的认知任务，与地面控制器（4.8％）相比，虚拟任务的性能与RPNI控制器（精度降低了1.1％）更加一致。当RPNI信号从肌内肌电图控制器中排除时(即仅残留肌肉),把握选择精度下降了24%。参与者使用地面控制器完成咖啡任务的完成时间比使用RPNI控制器长11.7%。与地面控制器相比，使用RPNI控制器时，她还完成了咖啡任务，最多可减少25个总错误中的11个过渡错误。意义。与残余肌肉和肌内电极配合使用RPNI信号可以提高虚拟和物理环境中的抓取选择准确性。这种方法在不需要重新校准的情况下产生一致的性能,同时减少与用于肌电控制的模式识别相关的认知负荷(临床试验登记号NCT03260400)。
