Abstract
Offline classification accuracy (CA) is a widely accepted measure to evaluate the performance in pattern recognition based myoelectric scheme. However, whether offline metrics are able to be transferred to evaluate or predict online performance is still unclear. In this study, the relationship between offline metrics and online metrics are analyzed. In offline scenario, global CA is biased, thus class-wise accuracy standard deviation (std) is proposed as a supplement. Target Achievement Control Test (TAC Test) is adopted in online scenario. Completion rate, completion time and path efficiency are considered as online metrics. Our results demonstrate that online completion rate is strongly correlated with offline global CA and class-wise accuracy std. The correlation between offline and online performance metrics indicates it is reasonable to develop efficient algorithm in offline scenario if both global CA and class-wise accuracy are considered.
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