Portnova-Fahreeva Alexandra A, Rizzoglio Fabio, Mussa-Ivaldi Ferdinando A, Rombokas Eric
Department of Mechanical Engineering, Northwestern University, Evanston, IL, United States.
Department of Neuroscience, Northwestern University, Chicago, IL, United States.
Front Bioeng Biotechnol. 2023 Jun 26;11:1134135. doi: 10.3389/fbioe.2023.1134135. eCollection 2023.
In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control. Here, we present a novel Autoencoder-based controller, in which the user is able to control a high-dimensional (17D) virtual hand via a low-dimensional (2D) space. We assess the efficacy of the controller via a validation experiment with four unimpaired participants. All the participants were able to significantly decrease the time it took for them to match a target gesture with a virtual hand to an average of and three out of four participants significantly improved path efficiency. Our results suggest that the Autoencoder-based controller has the potential to be used to manipulate high-dimensional hand systems via a myoelectric interface with a higher accuracy than PCA; however, more exploration needs to be done on the most effective ways of learning such a controller.
过去，诸如主成分分析等线性降维技术已被用于简化高维假手的肌电控制。尽管如此，它们的非线性对应技术，如自动编码器，已被证明在压缩和重建复杂的手部运动学数据方面更有效。因此，它们有可能成为更精确的假手控制工具。在此，我们提出一种基于自动编码器的新型控制器，用户能够通过低维（2D）空间控制高维（17D）虚拟手。我们通过对四名未受损参与者进行的验证实验来评估该控制器的有效性。所有参与者都能够显著缩短将虚拟手的目标手势匹配所需的时间，平均缩短至 ，并且四名参与者中有三名显著提高了路径效率。我们的结果表明，基于自动编码器的控制器有可能用于通过肌电接口以比主成分分析更高的精度操纵高维手部系统；然而，对于学习这种控制器的最有效方法还需要进行更多探索。
