he possibility that the performance of a combined model is generally higher than those of individual ones. However, the aim of offline analysis was to see the relative performance change with respect to different test arm positions on a fixed training position; we note that the performance difference between regression models did not significantly affect the main results of the offline analysis. Offline estimation performance was calculated with the multivariate R^2^ index \[[@pone.0186318.ref054]\], which has been widely used to estimate the performance of regression algorithms: $$R^{2}~ = ~1 - ~\frac{\left. {\sum_{d}{Var\left( \mathbf{y}_{d} \right.}} - {\hat{\mathbf{y}}}_{d} \right)}{\sum_{d}{Var\left( \mathbf{y}_{d} \right)}},~$$ where **y**~*d*~ and **ŷ**~*d*~ indicate the reference cursor positions of the *d*-th DoF (the position of the larger circle presented in the calibration session) and cursor positions estimated by the trained linear regression model, respectively. The numerator in the second term is the total mean square error, which is normalized by the variance of the reference cursor positions. Therefore, the maximal r-square value is one in case that the trained linear regression model completely predicts the true cursor positions. A negative r-square value is also possible when the prediction error is larger than the variance of the reference cursor positions.

To choose the two post-processing parameters (filter constant and velocity coefficient), two performance metrics were considered, e.g., completion rate and completion time. For the main experiment, additional five quantitative performance metrics were employed to assess various aspects of real-time myoelectric controllability, thereby comprehensively investigating the effect of arm position change and donning/doffing. The description of all seven performance metrics are given in [Table 1](#pone.0186318.t001){ref-type="table"}. For the throughput metric, the task difficulty index (TDI) was defined as a function of the target distance beca