## 4 Results
### Recognition Performance  
A well-known metric such as accuracy may provide misleading information in an imbalanced dataset. The bias of the score toward the majority class accuracy causes this issue. Thus, this study evaluated recognition performance using an average of recall metrics to minimize bias due to an imbalanced dataset. The average of recall encompassed calculating the recall for each impairment level after concatenating the actual and predicted levels in inter-subject cross-validation, followed by calculating the overall average across impairment levels. Additionally, the average recall score of minority classes is presented. In Table 4, gray cells represent the highest recall score for each movement. Four out of seven movements surpassed a recall of 0.50, namely ME, HG, PG, and SG. Additionally, the ISCV and DS-ISCV machine learning models performed well depending on the movement, with the highest recall achieved by SVM for the second mechanism (SVM_DS-ISCV) in MF, the first mechanism of MLP (MLP_ISCV) in HG, SVM_DS-ISCV in TA, SVM_ISCV in PG, MLP_ISCV in CG, and MLP_ISCV in SG. However, in ME, SVM_DS-ISCV and MLP_ISCV shared the same recall score of 0.70. In this case, the average recall score of the minority classes of the partial and none levels must be observed. The average recall scores of non-minority classes presented in Table 5 were calculated from the partial and none levels. In the ME movement, SVM_DS-ISCV achieved the highest score, making it the best model for recognizing non-majority classes. However, different outcomes occurred in HG and TA, with MLP_DS-ISCV achieving the highest recall score for non-majority classes in both movements. There was a marginal difference in the recall scores for the overall (Table 4) and non-majority (Table 5) classes in the HG movement due to the trade-off between the recognition of majority and non-majority classes. In the TA movement, despite the highest score being achieved by SVM_DS-ISCV (Table 4), for the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Movement**} & \multicolumn{3}{c}{**ISCV**} & \multicolumn{3}{c}{**DS-ISCV**} \\ \cline{2-7}  & **SVM** & **RF** & **MLP** & **SVM** & **RF** & **MLP** \\ \hline ME & 0.61 & 0.61 & 0.70 & 0.70 & 0.62 & 0.64 \\ \hline MF & 0.40 & 0.44 & 0.40 & 0.49 & 0.46 & 0.44 \\ \hline HG & 0.46 & 0.46 & 0.50 & 0.46 & 0.43 & 0.49 \\ \hline TA & 0.27 & 0.33 & 0.25 & 0.35 & 0.31 & 0.29 \\ \hline PG & 0.60 & 0.50 & 0.45 & 0.48 & 0.50 & 0.50 \\ \hline CG & 0.32 & 0.33 & 0.40 & 0.34 & 0.33 & 0.33 \\ \hline SG & 0.49 & 0.42 & 0.73 & 0.46 & 0.42 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average recall score of inter-subject cross-validation (gray: highest recall score of each movement). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Movement**} & \multicolumn{3}{c}{**Impairment Level**} \\ \cline{2-4}  & **Full** & **Partial** & **None** \\ \hline ME & 4246 & 491 & 1288 \\ \hline MF & 4601 & 431 & 1145 \\ \hline HG & 4522 & 382 & 640 \\ \hline TA & 4083 & 1001 & 614 \\ \hline PG & 4013 & 1156 & 604 \\ \hline CG & 5264 & 320 & 476 \\ \hline SG & 4825 & 438 & 689 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Total data points after the feature extraction process across subjects. non-majority class, MLP_DS-ISCV achieved the highest score. This was due to the high recall score for the majority class in TA. Considering the importance of the non-majority class, this study indicates the best model for each movement task with a dagger (\(\dagger\)) symbol in Table 5.  
Figure 9 shows detailed information in concatenated confusion matrices for each movement after inter-subject cross-validation. In general, SVM_DS-ISCV of ME and MLP_ISCV of SG achieved good recognition performance, with the predicted labels achieving more than 50% correctness compared with all true labels, as shown in Figure 9a,g respectively. Subsequently, SVM_DS-ISCV of MF, MLP_ISCV of HG, and SVM_ISCV of PG achieved more than 50% for two labels, as shown in Figure 9b,c,e respectively.