Shared Prosthetic Control Based on Multiple Movement Intent Decoders
Shared Prosthetic Control Based on Multiple Movement Intent Decoders
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DOI：
10.1109/tbme.2020.3045351
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发表时间：
2021-05-01
影响因子：
4.6
通讯作者：
Mathews, V. John
中科院分区：
文献类型：
--
作者：
Dantas, Henrique;Hansen, Taylor C.;Mathews, V. John
意义：文献中存在许多运动意图解码器，它们通常在使用的算法和生成的输出的性质方面有所不同。每种方法都有其自身的优点和缺点。组合多种算法的估计可能比任何单独的方法具有更好的性能。目的：本文提出并评估了基于多个意志运动意图解码器的假肢共享控制器框架。方法：本文开发了一种结合多种估计来控制假肢的算法。该方法的功能通过将基于卡尔曼滤波器的解码器与基于多层感知器分类器的解码器相结合的系统进行了验证。共享控制器的性能在在线实验中得到验证，其中虚拟肢体由截肢者和完整手臂受试者实时控制。在测试阶段，受试者使用卡尔曼滤波器解码器、多层感知器解码器或两者的线性组合实时控制虚拟手将数字移动到指示位置。结果：共享控制器在统计上比组件解码器有显着的改进。具体来说，一定程度的共享控制会导致目标时间度量的增加和意外运动的减少。结论：本文的共享控制器结合了本文中测试的组件解码器的优良品质。这里，将卡尔曼滤波器解码器与基于分类器的解码器相结合继承了卡尔曼滤波器解码器的灵活性以及来自基于分类器的解码器的有限的不需要的运动，从而产生能够更自然和可靠地执行日常生活任务的系统。
Significance: A number of movement intent decoders exist in the literature that typically differ in the algorithms used and the nature of the outputs generated. Each approach comes with its own advantages and disadvantages. Combining the estimates of multiple algorithms may have better performance than any of the individual methods. Objective: This paper presents and evaluates a shared controller framework for prosthetic limbs based on multiple decoders of volitional movement intent. Methods: An algorithm to combine multiple estimates to control the prosthesis is developed in this paper. The capabilities of the approach are validated using a system that combines a Kalman filter-based decoder with a multilayer perceptron classifier-based decoder. The shared controller's performance is validated in online experiments where a virtual limb is controlled in real-time by amputee and intact-arm subjects. During the testing phase subjects controlled a virtual hand in real time to move digits to instructed positions using either a Kalman filter decoder, a multilayer perceptron decoder, or a linear combination of the two. Results: The shared controller results in statistically significant improvements over the component decoders. Specifically, certain degrees of shared control result in increases in the time-in-target metric and decreases in unintended movements.Conclusion: The shared controller of this paper combines the good qualities of component decoders tested in this paper. Herein, combining a Kalman filter decoder with a classifier-based decoder inherits the flexibility of the Kalman filter decoder and the limited unwanted movements from the classifier-based decoder, resulting in a system that may be able to perform the tasks of everyday life more naturally and reliably.