Gigli Andrea, Gijsberts Arjan, Castellini Claudio
Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Weßling, Germany.
Vandal Laboratory, Istituto Italiano di Tecnologia, Genoa, Italy.
Front Bioeng Biotechnol. 2020 Apr 30;8:361. doi: 10.3389/fbioe.2020.00361. eCollection 2020.
Natural myocontrol is the intuitive control of a prosthetic limb via the user's voluntary muscular activations. This type of control is usually implemented by means of pattern recognition, which uses a set of training data to create a model that can decipher these muscular activations. A consequence of this approach is that the reliability of a myocontrol system depends on how representative this training data is for all types of signal variability that may be encountered when the amputee puts the prosthesis into real use. Myoelectric signals are indeed known to vary according to the position and orientation of the limb, among other factors, which is why it has become common practice to take this variability into account by acquiring training data in multiple body postures. To shed further light on this problem, we compare two ways of collecting data: while the subjects hold their limb statically in several positions one at a time, which is the traditional way, or while they dynamically move their limb at a constant pace through those same positions. Since our interest is to investigate any differences when controlling an actual prosthetic device, we defined an evaluation protocol that consisted of a series of complex, bimanual daily-living tasks. Fourteen intact participants performed these tasks while wearing prosthetic hands mounted on splints, which were controlled via either a statically or dynamically built myocontrol model. In both cases all subjects managed to complete all tasks and participants without previous experience in myoelectric control manifested a significant learning effect; moreover, there was no significant difference in the task completion times achieved with either model. When evaluated in a simulated scenario with traditional offline performance evaluation, on the other hand, the dynamically-trained system showed significantly better accuracy. Regardless of the setting, the dynamic data acquisition was faster, less tiresome, and better accepted by the users. We conclude that dynamic data acquisition is advantageous and confirm the limited relevance of offline analyses for online myocontrol performance.
自然肌电控制是指通过使用者的自主肌肉激活来直观地控制假肢。这种控制方式通常通过模式识别来实现，即利用一组训练数据创建一个能够解读这些肌肉激活信号的模型。这种方法的一个结果是，肌电控制系统的可靠性取决于该训练数据对于截肢者实际使用假肢时可能遇到的所有信号变异性的代表性如何。肌电信号确实会因肢体的位置和方向等因素而有所不同，这就是为什么通过在多种身体姿势下采集训练数据来考虑这种变异性已成为常见做法。为了进一步阐明这个问题，我们比较了两种数据收集方式：一种是让受试者一次将肢体静态地保持在几个位置，这是传统方式；另一种是让他们以恒定速度动态地将肢体移动通过相同的位置。由于我们感兴趣的是研究在控制实际假肢设备时的任何差异，我们定义了一个评估方案，该方案由一系列复杂的双手日常生活任务组成。十四名健全的参与者在佩戴安装在夹板上的假手执行这些任务时，假手通过静态或动态构建的肌电控制模型进行控制。在这两种情况下，所有受试者都成功完成了所有任务，并且之前没有肌电控制经验的参与者表现出了显著的学习效果；此外，两种模型完成任务的时间没有显著差异。另一方面，当在具有传统离线性能评估的模拟场景中进行评估时，动态训练的系统显示出明显更高的准确性。无论在哪种情况下，动态数据采集都更快、更轻松，并且更受用户欢迎。我们得出结论，动态数据采集具有优势，并证实了离线分析对于在线肌电控制性能的相关性有限。
