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基于协同启发的线性插值的稳定、同步、成比例的 4-DoF 假肢手控制:病例系列。

Stable, simultaneous and proportional 4-DoF prosthetic hand control via synergy-inspired linear interpolation: a case series.

机构信息

Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-1712, USA.

APT Center, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Blvd., Mail Stop 151 W/APT, Cleveland, OH, 44106-1702, USA.

出版信息

J Neuroeng Rehabil. 2021 Mar 18;18(1):50. doi: 10.1186/s12984-021-00833-3.

Abstract

BACKGROUND

Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control.

METHODS

Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability.

RESULTS AND CONCLUSIONS

In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7-9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times.

摘要

背景

目前的商业假肢手控制器限制了患者充分利用高自由度(DoF)假肢手的能力。现有的前馈控制器依赖于大型训练数据集进行控制器设置,并需要在佩戴假肢后重新校准。最近,一种直观、比例、同步、基于回归的 3-DOF 控制器通过将慢性植入的肌电图(ciEMG)电极与 K-最近邻(KNN)映射技术相结合,无需重新训练,可在几个月内保持稳定,无需重新训练。KNN 控制器的训练数据集要求随自由度呈指数增长,限制了 KNN 控制器在超过 3 个自由度的实际开发。我们假设,一种结合线性插值、肌肉协同框架和足够数量的 ciEMG 通道(每个自由度至少两个)的控制器,可以实现稳定的高自由度控制。

方法

两名经桡骨截肢患者 S6 和 S8 分别植入经皮接口双极肌内电极。在研究时,S6 和 S8 分别有 6 个和 8 个双极 EMG 电极。虚拟现实(VR)系统引导用户通过一个 3-DOF 和四个不同的 4-DOF 案例中的单个和成对训练运动。通过将肌电特征空间划分为由稳态运动肌电模式的向量限定的区域,构建用户活动的线性模型。控制器通过在线性插值周围训练的肌电运动的运动类别标签来评估在线 EMG 信号。这产生了一个同步、连续、直观和比例的控制器。通过在 3-DOF 和 4-DOF 中进行目标匹配任务来评估控制器,在该任务中,受试者控制虚拟手以匹配 80 个目标,这些目标覆盖了可用的运动空间。基于受试者的可用性,在 10 个月的时间内评估匹配百分比、到达目标的时间和路径效率。

结果和结论

在 3-DOF 中,S6 和 S8 在 8 个月和 10 个月后分别匹配了大多数目标,并表现出稳定的控制。在 4-DOF 中,两名受试者最初都找到了四个 4-DOF 控制器中的两个可用,匹配了大多数目标。S8 的 4-DOF 控制器稳定,在没有重新训练或在家中练习的情况下,7-9 个月的趋势有所改善。S6 的 4-DOF 控制器在没有重新训练的情况下 7 个月后不稳定。这些结果表明,在初始可行性和足够数量的肌电通道的情况下,本研究提出的控制器的性能可能保持稳定,甚至可能提高。总的来说,这项研究证明了一种控制器能够在无需重新训练或在家中使用且训练时间最少的情况下,在 3-DOF 中稳定、同步、比例、直观和连续地控制长达十个月,在 4-DOF 中控制长达九个月。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2522/7977328/e4c0340fabd2/12984_2021_833_Fig1_HTML.jpg

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