## 4 Discussion  
This research was conducted to analytically find a transferable deep learning (DL) model for ankle moment estimation by exploring 1302 different models, muscles, and feature combinations. Previous research focused on a single-model analysis [5; 8; 12; 15]. [PERSON] et al. [8] trained the same MLP model, but used six different training scenarios resulting in six models per volunteer all having the same architecture. The best model in [8] for normal gait was an NMS model (RMSE: \(0.08\pm 0.06\) Nm/kg), which demonstrated poorer performance than the RCNN (TA+SOL+GM+PB WL) model (RMSE: \(0.071\pm 0.011\) Nm/kg). [PERSON] et al. [11] trained four models per volunteer to estimate the ankle moment during standing, walking, running, and sprinting activities. On the other hand, [PERSON] et al. [13] trained 33 MLP models for a classification task by changing the selected muscles for the model input. Compared to the previously mentioned studies, our study focused solely on normal walking activities but trained 1302 different intrasubject DL models to find suitable models, muscles, and feature combinations for the task. To the best of our knowledge, this is the first study that attempts to find a suitable combination of models, features, and muscle selection for the lower extremities moment estimation. The architectures of the three selected models were designed to have a few layers and parameters to avoid overfitting the data and reduce computational costs for fast training and prediction. Indeed, the largest MLP model had 2609 trainable parameters, while the largest RCNN model and LSTM models had 2101 and 1865, respectively. The MLP, CNN, neural ODE, and LSTM models from [11] had 30,642, 156,264, and 32,306 trainable parameters, respectively. Comparing these parameter counts to our RCNN (TA+SOL+GM+PB WL) model, which had only 757 parameters, our best model had an intrasubject RMSE of \(0.071\pm 0.011\) Nm/kg, achieving similar performance to the LSTM model in [11] (RMSE: \(0.07\pm 0.02\) Nm/kg) with only 2.3% of the total parameter count. The study found that signal energy information features, such as WL, are sufficient for ankle moment estimation if the correct muscles are chosen, similar to the results found in [10] for sEMG-based models for hand classification. In contrast, signal frequency information features such as ZC and predictive model features such as AR coefficients make it hard to extract meaningful information to describe the moment. While most previous studies focused on the sEMG signals, we also explored the differentiated surface electromyography (DEMG) feature domains. We found that DEMG features result in low performance in the regression task. Previous studies such as [5; 8; 11; 12; 15] predetermined muscles, and feature selection, but the selection combination is indeed an essential factor for model performance. Models trained on the wrong muscle group (TA+PB only) had low performance re  
Figure 6: This figure compares the three intersubject models against their corresponding ankle moments from the inverse dynamic (ID) Equation (7). The x-axis represents the normalized gait cycle, starting from the swing phase and ending with the subsequent swing phase. The y-axis shows the normalized ankle moment. Positive values indicate plantar flexion, while negative values indicate dorsiflexion. The shaded areas represent the standard deviation of the estimations across subjects. S6 and S7 refer to volunteers number 6 and 7, respectively, tibialis anterior (TA), soleus (SOL), gastrocnemius medialis (GM), and peroneus brevis (PB) musclesgarding the selected features and models' architecture. The RCNN (TA+GM AR) and RCNN (TA+SOL+GM+PB AR) were models both trained on the same time-domain features but with different muscle selections; the former had a minimum \(R^{2}\) of \(-2\%\), while the latter scored a max \(R^{2}\) of 96.56%. Two of the top three intersubject models and the best intrasubject models used all four targeted shank muscles in the study.