## 4 Discussion
Using more muscles showed better results as it helps the model learn synergistic actions from different sources, resulting in more varied information fed to the trained model, whereas using a set of power features might introduce highly correlated information rather than capturing the moment effectively. MLP models tested in this study had high performance; however, they came with the cost of many parameters, such as needing to learn the time pattern and overfitting. On the other hand, LSTM is known for its use in the time-series models, but in this study, its performance was not better than the MLP models. Using RCNN allows the model to learn new features from existing ones before proceeding to the LSTM layers, resulting in better moment estimation. Another factor that affects the DL models is the volunteer data. Models trained using sEMG features had distinct results from one volunteer to another. For example, the intersubject RCNN (TA+GM+Pb MAV+WL) model achieved an average \(R^{2}\) score of 92.89% when tested on S5 but only achieved 41.21% when the model was tested on S7. The nominated models for the intersubject model creation had RCNN architecture and WL features. Having the convolutional layer before the recurrent layers allowed the model to extract complex features from the extracted features. Both Intersubject models performed less than their related intrasubject models. A similar observation was reported in [16] when using DL to estimate knee kinematics and vertical GRF from IMU sensors. The best intersubject in this study was the RCNN (TA+SOL+GM+Pb WL) had achieved \(R^{2}\) of 94.37%, RMSE of 0.061, and NRMSE of 0.093 using all selected muscles groups similar to the finding in the classification study in [13]. The best DEMG model RCNN (TA+SOL+GM+PB RMS+MAV) achieved (\(R^{2}\): 95.86% \(\pm\) 1.80%, NRMSE: 0.0504 \(\pm\) 0.0128) also used all muscles with the RCNN model architecture, but did not have the WL feature. [PERSON] et al. [6] created MLP and LSTM models using simulated IMU data from a motion capture system, achieving an NRMSE of 0.0739 Nm/kg and 0.0732 Nm/kg, respectively. [PERSON] et al. [15] used volunteers' anthropometrics and kinematics information from a motion capture system in a CNN model, achieving an \(R^{2}\) of \(92\%\pm 3\%\). While these models outperformed our RCNN (TA+SOL+GM+Pb WL) intersubject model (\(R^{2}\): 88.92% \(\pm\) 4.14%, RMSE: 0.137 \(\pm\) 0.027 Nm/kg), they used motion capture systems to generate the input data, which had limited walking space compared to the sEMG. Despite achieving high results for the ankle moment estimation, the current study has some limitations:  
* The results are based on OpenSim ID tools, and the ID results are assumed to be accurate. * The walking space was relatively small, causing volunteers some difficulty in maintaining natural walking during the recording period. * The study focused on normal gait and did not include other daily activities such as sitting, standing, and jumping. * The study focused on training small DL models, and did not examine the impact of increasing model dimensions. * The volunteers were asked to walk barefoot; wearing shoes or sports equipment can affect the activation level of the muscles [24; 25]. * Only four muscles were used for building the models. * The study was conducted on healthy males in their 20s. * Injured, aged, and amputee populations are the main targets for rehabilitation, yet the models were not tested on them.