Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, Pavia, Italy.
J Neuroeng Rehabil. 2012 Jun 15;9:40. doi: 10.1186/1743-0003-9-40.
In spite of the advances made in the design of dexterous anthropomorphic hand prostheses, these sophisticated devices still lack adequate control interfaces which could allow amputees to operate them in an intuitive and close-to-natural way. In this study, an anthropomorphic five-fingered robotic hand, actuated by six motors, was used as a prosthetic hand emulator to assess the feasibility of a control approach based on Principal Components Analysis (PCA), specifically conceived to address this problem. Since it was demonstrated elsewhere that the first two principal components (PCs) can describe the whole hand configuration space sufficiently well, the controller here employed reverted the PCA algorithm and allowed to drive a multi-DoF hand by combining a two-differential channels EMG input with these two PCs. Hence, the novelty of this approach stood in the PCA application for solving the challenging problem of best mapping the EMG inputs into the degrees of freedom (DoFs) of the prosthesis.
A clinically viable two DoFs myoelectric controller, exploiting two differential channels, was developed and twelve able-bodied participants, divided in two groups, volunteered to control the hand in simple grasp trials, using forearm myoelectric signals. Task completion rates and times were measured. The first objective (assessed through one group of subjects) was to understand the effectiveness of the approach; i.e., whether it is possible to drive the hand in real-time, with reasonable performance, in different grasps, also taking advantage of the direct visual feedback of the moving hand. The second objective (assessed through a different group) was to investigate the intuitiveness, and therefore to assess statistical differences in the performance throughout three consecutive days.
Subjects performed several grasp, transport and release trials with differently shaped objects, by operating the hand with the myoelectric PCA-based controller. Experimental trials showed that the simultaneous use of the two differential channels paradigm was successful.
This work demonstrates that the proposed two-DoFs myoelectric controller based on PCA allows to drive in real-time a prosthetic hand emulator into different prehensile patterns with excellent performance. These results open up promising possibilities for the development of intuitive, effective myoelectric hand controllers.
尽管在灵巧拟人手假肢设计方面取得了进展，但这些复杂的设备仍然缺乏足够的控制接口，无法让截肢者以直观、接近自然的方式操作它们。在这项研究中，使用由六个电机驱动的拟人五指机器人手作为假肢模拟器，评估基于主成分分析（PCA）的控制方法的可行性，该方法专门针对这个问题而设计。由于之前已经证明前两个主成分（PC）可以很好地描述整个手的配置空间，因此这里使用的控制器反转了 PCA 算法，并通过将两个差分通道肌电输入与这两个 PC 结合起来，允许驱动多自由度手。因此，这种方法的新颖之处在于将 PCA 应用于解决将肌电输入最佳映射到手假肢自由度的具有挑战性的问题。
开发了一种具有临床可行性的双自由度肌电控制器，利用两个差分通道，十二名健全参与者分为两组，自愿在手的简单抓握试验中使用前臂肌电信号进行控制。测量任务完成率和时间。第一个目标（通过一组参与者评估）是了解该方法的有效性；即是否可以实时、以合理的性能在不同的抓握中驱动手，同时利用移动手的直接视觉反馈。第二个目标（通过不同的组评估）是研究直观性，因此评估在连续三天内的性能统计差异。
参与者通过使用基于 PCA 的肌电双自由度控制器对手进行操作，进行了几次具有不同形状的物体的抓握、运输和释放试验。实验结果表明，同时使用两个差分通道范式是成功的。
这项工作表明，所提出的基于 PCA 的双自由度肌电控制器能够实时驱动假肢模拟器进入不同的抓握模式，具有出色的性能。这些结果为开发直观、有效的肌电手控制器开辟了有前途的可能性。
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