## 3 Results
### Observed Network Performance  
With seven hand gestures available for testing, this study aimed to evaluate the network's classification capabilities using DL and to examine the effect of bilateral data fusion in EMG signal processing for datasets A and B. In addition to all-in-one gesture prediction (7G-A and 7G-B models), the labeled data were organized into gesture subsets ranging from two to seven classes, enabling assessment across varying levels of classification complexity. To highlight potential real-world applications, gestures were selected to represent distinct muscle activation patterns commonly observed in paretic movements or movement attempts. This subset-splitting strategy is widely used in hand gesture recognition research [9, 13, 26]. For the two-gesture subsets (2G-A and 2G-B sub-models), the rest state and wrist extension were included. The three-gesture subsets (3G-A and 3G-B sub-models) consisted of rest, wrist extension, and thumbs up. The four-gesture subsets (4G-A and 4G-B sub-models) comprised rest, fist, wrist flexion, and wrist extension. For the five-gesture subsets (5G-A and 5G-B sub-models), rest, index pinch, wrist flexion, wrist extension, and thumbs up were selected. The six-gesture subsets (6G-A and 6G-B sub-models) included all gestures except the fist. Additionally, to address potential limitations associated with the rest state (i.e., its classification may not fully capture distinct movement actions), six-gesture subsets excluding the rest state (6G-NR-A and 6G-NR-B sub-models) were evaluated. The performances of individual gesture labels, based on machine learning metrics, are listed in Supplementary Tables S1 and S2. The classification performance of the CNN-LSTM model across these gesture sets, along with the results of statistical comparisons between the paretic-only and bilateral fusion datasets, are presented in Tables 2 and 3. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Clinical Trial**} & **Patient No.** & **Age** & **Sex** & \begin{tabular}{c} **Stroke** \\ **Type** \\ \end{tabular} & \
\begin{tabular}{c} **Onset** \\ **(Days)** \\ \end{tabular} & **FMA-UE** &
\begin{tabular}{c} **Paretic** \\ **Extremity** \\ \end{tabular} \\ \hline \multirow{6}{*}{R04-041 (measurements from session A were allocated to dataset A, and measurements from session B were allocated to dataset B)} & S020-A & \multirow{2}{*}{75} & \multirow{2}{*}{M} & \multirow{2}{*}{CI} & 24 & 10 & \multirow{2}{*}{L} \\  & S020-B & & & & 45 & 10 & \\ \cline{1-1} \cline{3-8}  & S021-A & & & & 26 & 10 & \\ \cline{1-1} \cline{3-8}  & S021-B & & & & 34 & 13 & \\ \cline{1-1} \cline{3-8}  & S022-A & & & & 25 & 15 & \\ \cline{1-1} \cline{3-8}  & S022-B & & & & 34 & 24 & \\ \cline{1-1} \cline{3-8}  & S023-A & & & & 23 & 55 & \\ \cline{1-1} \cline{3-8}  & S023-B & & & & 31 & 56 & \\ \cline{1-1} \cline{3-8}  & S024-A & & & & 34 & 47 & \\ \cline{1-1} \cline{3-8}  & S024-B & & & & 47 & 54 & \\ \cline{1-1} \cline{3-8}  & S025-A & & & & 31 & 16 & \\ \cline{1-1} \cline{3-8}  & S025-B & & & & 50 & 17 & \\ \hline \hline \end{tabular}  
* M—male; F—female, Cl—cerebral infarction; ICH— intracranial hemorrhage; R—right; L—left); FMA-UE—Fugly-Meyer Assessment of Upper Extremity Scale. Dataset A includes subjects S001 to S019 and the ‘A’ measurements for subjects S020 through S025 (S020A, S021A, etc.). Dataset B includes the same subjects, from S001 to S019, and the ‘B’ measurements for subjects S020 through S025 (S020B, S021B, etc.).  
\end{table} 
Table 1: _Cont._