Amsuess Sebastian, Goebel Peter, Graimann Bernhard, Farina Dario
IEEE Trans Neural Syst Rehabil Eng. 2015 Sep;23(5):827-36. doi: 10.1109/TNSRE.2014.2361478. Epub 2014 Oct 3.
Functional replacement of upper limbs by means of dexterous prosthetic devices remains a technological challenge. While the mechanical design of prosthetic hands has advanced rapidly, the human-machine interfacing and the control strategies needed for the activation of multiple degrees of freedom are not reliable enough for restoring hand function successfully. Machine learning methods capable of inferring the user intent from EMG signals generated by the activation of the remnant muscles are regarded as a promising solution to this problem. However, the lack of robustness of the current methods impedes their routine clinical application. In this study, we propose a novel algorithm for controlling multiple degrees of freedom sequentially, inherently proportionally and with high robustness, allowing a good level of prosthetic hand function. The control algorithm is based on the spatial linear combinations of amplitude-related EMG signal features. The weighting coefficients in this combination are derived from the optimization criterion of the common spatial patterns filters which allow for maximal discriminability between movements. An important component of the study is the validation of the method which was performed on both able-bodied and amputee subjects who used physical prostheses with customized sockets and performed three standardized functional tests mimicking daily-life activities of varying difficulty. Moreover, the new method was compared in the same conditions with one clinical/industrial and one academic state-of-the-art method. The novel algorithm outperformed significantly the state-of-the-art techniques in both subject groups for tests that required the activation of more than one degree of freedom. Because of the evaluation in real time control on both able-bodied subjects and final users (amputees) wearing physical prostheses, the results obtained allow for the direct extrapolation of the benefits of the proposed method for the end users. In conclusion, the method proposed and validated in real-life use scenarios, allows the practical usability of multifunctional hand prostheses in an intuitive way, with significant advantages with respect to previous systems.
利用灵巧的假肢装置实现上肢功能替代仍然是一项技术挑战。虽然假手的机械设计发展迅速,但人机接口以及激活多个自由度所需的控制策略尚不够可靠,难以成功恢复手部功能。能够从残余肌肉激活产生的肌电信号中推断用户意图的机器学习方法被视为解决这一问题的有前景的方案。然而,当前方法缺乏鲁棒性,阻碍了它们在临床中的常规应用。在本研究中,我们提出了一种新颖的算法,用于顺序、固有比例且高鲁棒性地控制多个自由度,从而实现较好水平的假手功能。该控制算法基于与幅度相关的肌电信号特征的空间线性组合。此组合中的加权系数源自共同空间模式滤波器的优化准则,该准则可实现运动之间的最大可区分性。本研究的一个重要组成部分是对该方法的验证,验证在健全受试者和截肢受试者身上均有进行,这些受试者使用定制套筒的物理假肢,并进行了三项模拟不同难度日常生活活动的标准化功能测试。此外,在相同条件下,将新方法与一种临床/工业方法以及一种学术先进方法进行了比较。在需要激活多个自由度的测试中,新算法在两个受试者组中均显著优于现有技术。由于对健全受试者和佩戴物理假肢的最终用户(截肢者)进行了实时控制评估,所获得的结果使得能够直接推断所提出方法对最终用户的益处。总之,在实际使用场景中提出并验证的该方法,以直观的方式实现了多功能手部假肢的实际可用性,相对于先前系统具有显著优势。