## 3 Results
### AUC-ROC Visualization for Paretic and Fused EMG Classification  
To improve clarity, AUC-ROC plots were used to illustrate the classification performance of each CNN-LSTM network across EMG gesture models. The multi-curve plots are arranged in pairs: the left panels display results for paretic data from datasets A and B, while the right panels represent the corresponding outcomes for the fused datasets. Each ROC curve is shown in a distinct color to represent a specific gesture class, and all plots are derived from cross-validated classification results (see Figures 7 and 8). Based on the cross-validation results, several important trends were identified in the prediction of paretic gestures, both with and without bilateral data fusion. The results highlight the main observations derived from the comparison of 14 gesture models across datasets A and B (seven paired models per dataset). These findings are supported by the ML metrics (Tables 2 and 3) and gesture-specific ROC curves with corresponding AUC values (Figures 7 and 8). First, dataset B demonstrates better performance than dataset A, which includes EMG data from an earlier post-stroke period, with one exception. This trend is reflected in higher SENS, SP, ACC, and F1 metrics in dataset B (see Table 3) and is further supported by the ROC curves and AUC values (Figures 7 and 8). Interestingly, the exceptional case showed that bilateral fusion of the rest and wrist extension gesture pair resulted in significantly lower performance than DNN predictions based solely on paretic data (unpaired _t_-test, \(p\) = 0.045). Second, except for one specific gesture pair, bilateral data fusion significantly enhances DNN recognition performance for paretic hand movements. This improvement is statistically validated, with significance levels increasing as the complexity of EMG cluster recognition grows (i.e., when more hand gesture classes are included in the classification). Additionally, the ROC curves for both the paretic-only and fused datasets confirm this trend, showing consistent classification patterns as the gesture class count increases (Figures 7 and 8). Within each gesture subset, the same gestures tend to be dominant or inferior: the rest gesture typically yields the highest AUC, whereas the fist or thumbs up gesture class most often yields the lowest. Third, an exception to performance gains with increasing gesture classes is observed in specific subsets: rest, wrist extension, and thumbs up in dataset A; and rest, wrist flexion, wrist extension, and fist in dataset B. In these cases, both the paretic and fused datasets exhibit the lowest classification performance among seven pairs of gesture subsets for each dataset. Specifically, the accuracy was 73.01 \(\pm\) 9.20% (paretic) and 78.42 \(\pm\) 7.76% (fused) in dataset A, and 77.20 \(\pm\) 5.82% (paretic) and 78.78 \(\pm\) 4.31% (fused) in dataset B. Fourth, both datasets exhibit generally high SP values, ranging from 80% to 89% with a low standard deviation (SD) of 2-3%, except in the described-above two-gesture model (rest and wrist extension). In dataset A, the SP decreased to 66.69 \(\pm\) 16.53% for paretic data and 64.31 \(\pm\) 11.9% for fused data; in dataset B, it dropped to 57.09 \(\pm\) 10.35% for paretic and 62.00 \(\pm\) 10.79% for fused data. Fifth, despite accuracy drops for the specific gestures of thumbs up, wrist extension, and rest in dataset A, and rest, wrist flexion, wrist extension, and fist in dataset B, the F1 consistently outperformed other metrics across all gesture classes with bilateral data fusion (compared to paretic-only). These gains were statistically significant, with F1 increases of up to 9% in dataset B and 13% in dataset A. Finally, excluding the rest gesture class from classification, as tested and validated (see Figures 7m,n and 8m,n), did not significantly impact CNN-LSTM performance. The results were comparable to the six-gesture model, with both datasets achieving approximately 80% accuracy and stable SENS, SP, and F1 metrics. Figure 7: _Cont._  
Figure 8: \(Cont\).