## 3 Results
### Intersubject Model  
Following the intrasubject models' results, selected RCNN models were then trained for each volunteer using the leave-one-out approach. Table 3 shows the results of each trained intersubject model. The volunteers column in the table refers to the volunteers left out during the training and used for the evaluation. The summary row shows the mean and standard deviation of each intersubject model and metrics. RCNN (TA+SOL+GB+PB WL) intersubject models outperformed their corresponding RCNN (TA+GM+PB MAV+WL) models except for models evaluated on S1 and S3. Intersubject models were trained to learn unknown volunteer gait patterns, resulting in less accurate models than the intrasubject models; a similar observation was found in [16]. Unlike the intrasubject model cases, the model learns the volunteer's walking patterns before testing them. The intersubject models' results varied based on the volunteers' data used for training, which resulted in a low \(R^{2}\) average with high variance, especially for RCNN (TA+GM+PB MAV+WL) models. Both selected models' architectures find difficulties estimating the S7 ankle moment.  
RCNN (TA+SOL+GM+PB WL) models evaluated on S5 and S6 had similar results with S6 \(R^{2}\) (94.37%) greater than S5 \(R^{2}\) (94.13%) by 0.24%, but S5 had NRMSE (0.059) less than S6 NRMSE (0.061) by 0.002 only. Looking at the RMSE, S6 (body mass = 69.5 kg) had RMSE (\(0.093*69.5=6.46\) Nm) less than S5 (body mass = 69.6 kg) RMSE (\(0.099*69.6=6.89\) Nm). Figure 6 shows S6 RCNN (TA+SOL+GM+PB WL) intersubject model (best model) and both S7 intersubject models ID results against the estimated moment. The S6 model had a small offset during the first swing phase and could not estimate the peak plantar flexion moment value. However, it was able to synchronize the stance phase moment estimation with the ID moment and also estimate the dorsiflexion moment at the end of the stance phase.  
S7 ID planter moment increased between \"35% to 40% of the record period\". At the same time, the RCNN (TA+GM+PB MAV+WL) model slowly decreased the dorsiflexion moment resulting in a gap between the ID moment and estimation. In contrast, the RCNN (TA+SOL+GM+PB WL) estimated this change. The ID planter moment slowly increased after 40% until reaching the peak planter moment. In contrast, both RCNN models had rapid increases simultaneously, resulting in a gap between the ID moment and the model's estimated moment during the first part of the stance phase. In summary, neither model can adapt to the volunteer moment's rate of change. For the remaining part, both RCNN models' moments were slightly ahead of the ID moment. They could both estimate the toe-off dorsiflexion moment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Volunteer**} & \multicolumn{2}{c}{**RCNN (TA+SOL+GM+Pb WL)**} & \multicolumn{2}{c}{**RCNN (TA+GM+Pb MAV+WL)**} \\  & \(R^{2}\) & **NRMSE** & **RMSE [Nm/kg]** & \(R^{2}\) & **NRMSE** & **RMSE [Nm/kg]** \\ \hline S1 & 86.85\% & 0.092 & 0.169 & 91.00\% & 0.076 & 0.139 \\ S2 & 90.23\% & 0.082 & 0.150 & 89.41\% & 0.085 & 0.156 \\ S3 & 87.43\% & 0.088 & 0.152 & 89.69\% & 0.079 & 0.138 \\ S4 & 87.83\% & 0.094 & 0.153 & 78.25\% & 0.126 & 0.205 \\ S5 & 94.13\% & 0.059 & 0.099 & 92.89\% & 0.065 & 0.109 \\ S6 & 94.37\% & 0.061 & 0.093 & 90.66\% & 0.078 & 0.119 \\ S7 & 81.59\% & 0.098 & 0.140 & 41.21\% & 0.175 & 0.250 \\ \hline Summary & 88.92\% \(\pm\) 4.14\% & 0.082 \(\pm\) 0.015 & \(0.137\pm 0.027\) & \(81.87\%\pm 17.18\%\) & \(0.098\pm 0.036\) & \(0.159\pm 0.047\) \\ \hline \end{tabular}
\end{table}
Table 3: Only 2 sEMG recurrent convolutional neural network (RCNN) models out of 1302 models were transferred for the intrasubject analysis. The first model was trained by extracting the waveform length (WL) features from the tibialis anterior (TA), soleus (SOL), gastrocnemius medialis (GM), and peroneus brevis (PB) muscles. The second model was trained by extracting the mean absolute value (MAV) and WL features from TA, GM, and PB muscles.