Pattern recognition techniques leveraging the use of electromyography signals have become a popular approach to provide intuitive control of myoelectric devices. Performance of these control interfaces is commonly quantified using offline classification accuracy, despite studies having shown that this metric is a poor indicator of usability. Researchers have identified alternative offline metrics that better correlate with online performance; however, the relationship has yet to be fully defined in the literature. This has necessitated the continued trial-and-error-style online testing of algorithms developed using offline approaches. To bridge this information divide, we conducted an exploratory study where thirty-two different metrics from the offline training data were extracted. A correlation analysis and an ordinary least squares regression were implemented to investigate the relationship between the offline metrics and six aspects online use. The results indicate that the current offline standard, classification accuracy, is a poor indicator of usability and that other metrics may hold predictive power. The metrics identified in this work also may constitute more representative evaluation criteria when designing and reporting new control schemes. Furthermore, linear combinations of offline training metrics generate substantially more accurate predictions than using individual metrics. We found that the offline metric feature efficiency generated the best predictions for the usability metric throughput. A combination of two offline metrics (mean semi-principal axes and mean absolute value) significantly outperformed feature efficiency alone, with a 166% increase in the predicted R2 value (i.e., VEcv). These findings suggest that combinations of metrics could provide a more robust framework for predicting usability.

中文翻译：

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预测肌电控制可用性的多变量方法


利用肌电信号的模式识别技术已成为提供肌电设备直观控制的流行方法。这些控制界面的性能通常使用离线分类精度来量化，尽管研究表明该指标并不是可用性的一个糟糕指标。研究人员已经确定了与在线表现更好相关的替代离线指标；然而，这种关系尚未在文献中得到充分定义。这就需要对使用离线方法开发的算法进行持续的试错式在线测试。为了弥合这一信息鸿沟，我们进行了一项探索性研究，从离线训练数据中提取了 32 个不同的指标。采用相关分析和普通最小二乘回归来研究离线指标与在线使用的六个方面之间的关系。结果表明，当前的离线标准（分类准确性）是可用性的一个较差指标，而其他指标可能具有预测能力。在设计和报告新的控制方案时，本工作中确定的指标也可能构成更具代表性的评估标准。此外，离线训练指标的线性组合比使用单独的指标产生更准确的预测。我们发现离线度量特征效率生成了可用性度量吞吐量的最佳预测。两个离线指标（平均半主轴和平均绝对值）的组合显着优于单独的特征效率，预测的 R2 值（即 VEcv）增加了 166%。 这些发现表明，指标的组合可以为预测可用性提供更强大的框架。