Wurth Sophie M, Hargrove Levi J
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:3630-3. doi: 10.1109/EMBC.2013.6610329.
Few studies have directly compared real-time control performance of pattern recognition to direct control for the next generation of myoelectric controlled upper limb prostheses. Many different implementations of pattern recognition control have been proposed, with minor differentiations in the feature sets and classifiers. An objective and generalizable evaluation tool quantifying the control performance, other than classification accuracy, is needed. This paper used the implementation of such a tool through the design of a target acquisition test, similar to a Fitts' law test, relating movement time of the target acquisition to the difficulty of the target, for a given control strategy. Performance metrics such as throughput (bits/sec), completion rate (%) and path efficiency (%) allow for a complete evaluation of the described strategies. We compared direct control and pattern recognition control with the proposed test and found that 1) the test was valid for control system evaluation by following Fitts' law with high coefficients of determination for both types of control and 2) that pattern recognition significantly outperformed direct control in throughput with similar completion rates and path efficiencies. In this framework, the present pilot study supports pattern recognition as a promising strategy and forms a basis for the development of a general and objective tool for the performance evaluation of upper limb control strategies.
很少有研究直接比较模式识别与直接控制在下一代肌电控制上肢假肢中的实时控制性能。已经提出了许多不同的模式识别控制实现方式，在特征集和分类器方面存在细微差异。除了分类准确率之外，还需要一种客观且可推广的评估工具来量化控制性能。本文通过设计一个目标获取测试来实现这样一种工具，该测试类似于菲茨定律测试，对于给定的控制策略，将目标获取的运动时间与目标难度相关联。诸如吞吐量（比特/秒）、完成率（%）和路径效率（%）等性能指标能够对所描述的策略进行全面评估。我们通过所提出的测试比较了直接控制和模式识别控制，发现：1）该测试对于控制系统评估是有效的，两种控制类型的决定系数都很高，符合菲茨定律；2）在完成率和路径效率相似的情况下，模式识别在吞吐量方面显著优于直接控制。在此框架下，本初步研究支持模式识别作为一种有前景的策略，并为开发用于上肢控制策略性能评估的通用且客观的工具奠定了基础。
