背景：各种人机界面（HMI）用于控制假肢，比如机器人的手。有希望的HMI之一是力Myography（FMG）。先前的研究表明，使用高密度FMG（HD-FMG）可以提高假体控制的准确性。动机：FMG控制系统中使用的传感器越多，系统变得越复杂和昂贵。这项研究提出了一种设计方法，该方法可以使用更少的传感器生产具有与HD-FMG控制系统相当的性能的动力假体。HD-FMG设备将仅用于在设计阶段从用户收集信息。然后将信道选择应用于所收集的数据,以确定对设备性能至关重要的传感器的数量和位置。这项研究评估了为此目的使用多通道选择（CS）方法。方法：在本案例研究中，使用了三个数据集。这些数据集是从嵌入在经桡骨截肢的受试者的内窝中的力敏电阻器收集的。当受试者执行六次手势的五次重复时收集传感器数据。然后使用收集的数据来评估五种CS方法：具有两种不同停止标准的顺序正向选择（SFS），最小冗余-最大相关性(mRMR)，遗传算法(GA)，还有Boruta.结果：五种方法中有三种(mRMR，GA,和Boruta）能够显着减少通道数量，同时在所有数据集中保持分类准确性。在所有数据集中，它们都没有超过其他两个。然而,GA导致所有三个数据集中的最小通道子集。还在稳定性方面比较了三种选定的方法[即，通过该方法选择的通道子集的一致性，因为引入了新的训练数据或删除了一些训练数据(Chandrashekar和Sahin，2014)].当应用于本研究的数据集时，与GA相比，Boruta和mRMR的不稳定性较小。结论：这项研究表明了使用所提出的设计方法的可行性，该方法可以生产比HD-FMG系统更简单但性能与它们相当的假肢系统。
Background: Various human machine interfaces (HMIs) are used to control prostheses, such as robotic hands. One of the promising HMIs is Force Myography (FMG). Previous research has shown the potential for the use of high density FMG (HD-FMG) that can lead to higher accuracy of prosthesis control. Motivation: The more sensors used in an FMG controlled system, the more complicated and costlier the system becomes. This
study proposes a design method that can produce powered prostheses with performance comparable to that of HD-FMG controlled systems using a fewer number of sensors. An HD-FMG apparatus would be used to collect information from the user only in the design phase. Channel selection would then be applied to the collected data to determine the number and location of sensors that are vital to performance of the device. This
study assessed the use of multiple channel selection (CS) methods for this purpose. Methods: In this case
study, three datasets were used. These datasets were collected from force sensitive resistors embedded in the inner socket of a subject with transradial amputation. Sensor data were collected as the subject carried out five repetitions of six gestures. Collected data were then used to asses five CS methods: Sequential forward selection (SFS) with two different stopping criteria, minimum redundancy-maximum relevance (mRMR), genetic algorithm (GA), and Boruta. Results: Three out of the five methods (mRMR, GA, and Boruta) were able to decrease channel numbers significantly while maintaining classification accuracy in all datasets. Neither of them outperformed the other two in all datasets. However, GA resulted in the smallest channel subset in all three of the datasets. The three selected methods were also compared in terms of stability [i.e., consistency of the channel subset chosen by the method as new training data were introduced or some training data were removed (Chandrashekar and Sahin, 2014)]. Boruta and mRMR resulted in less instability compared to GA when applied to the datasets of this
study. Conclusion: This
study shows feasibility of using the proposed design method that can produce prosthetic systems that are simpler than HD-FMG systems but have performance comparable to theirs.
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