## 3 Results  
### Intrasubject Models  
#### 3.1.1 sEMG Models  
Figure 2(a) shows the results distribution of the models trained on AR or ZC features with a max of two features (due to space limitation). Each boxplot contains the results from 49 different intrasubject models. Models trained with either AR or ZC features only tended to have high variance in results depending on the muscles selected, DL model and volunteer with wide \(R^{2}\) range from \(-2\%\) for RCNN (TA+GM AR) model trained on S3 to 95.65% for LSTM (TA+SOL+GM+PB AR) model trained on S5. Combining the two features reduces the variance in data. It helped improve LSTM models, yet the results had less variance than the other trained models. Figure 2(b) shows the results of models trained using signal power features only (RMS, MAV, and WL). The figure has a single outlier for each model-features pair, which can also be found in Figure 2(a) in 17 out of 26 boxplots. Figure 2(c) shows the distribution of the results per muscle group using signal power features. All groups had an \(R^{2}\) median greater than 90% except for TA+PB models, which had lower estimation results, causing the outliers in Figure 2(b). Out of the 1302 trained models, the top three models were all sEMG models that achieved an average \(R^{2}\) greater than 96% and NRMSE average less than 0.05 with a standard deviation less than 1.5%, and 0.09, respectively. Table 2 shows the results of the top three sEMG models for each volunteer. The summary row shows the mean and standard deviation for each model and metric. RCNN (TA+GM+PB MAV+WL) model was superior to the MLP (TA+SOL+GM+PB MAV+WL) model. The RCNN (TA+SOL+GM+PB WL) model had higher intersubject variance than the other two models. However, its average values (\(R^{2}\): 96.89%, NRMSE: 0.0434) were the best across all other sEMG models. LSTM (TA+SOL+GM+PB MAV+WL) was the only LSTM model able to fulfill the average criteria. However, the intersubject variance was high (\(R^{2}\): 96.44% \(\pm\) 0.0151, NRMSE: 0.0464 \(\pm\) 0.0103), suggesting that the model failed to learn the gait pattern of some volunteers properly, which makes it unlikely to work well for the intersubject model case. Figure 3: sEMG intrasubject models results. (**a**) boxplots illustrate the distribution of \(R^{2}\) values for models trained on various combinations of autoregressive model (AR) coefficients and zero crossing count (ZC) features. (**b**) illustrates the distribution of \(R^{2}\) values for models trained on Signal Power features: root mean square (RMS), mean absolute value (MAV), and waveform length (WL). (**c**) shows the distribution of \(R^{2}\) values for models for tibialis anterior (TA), soleus (SOL), gastrocnemius medialis (GM), and peroneus brevis (PB) muscles groups when using Signal Power features. The outliers observed in (**b**) are primarily attributed to the TA+PB muscle group, which exhibited lower estimation performance than other muscle groups. Figure 4 shows the ID moments against the three models mentioned above estimation for S1. The MLP model estimation (\(R^{2}\): 95.14%, NRMSE: 0.056, RMSE: 0.103 Nm/kg) had an offset value during the first swing phase, failed to estimate the positive dorsiflexor moment at the start and end of the stance phase, and estimated the stance phase moment earlier than the ID moments without being able to match the plantar flexor moment peak. The RCNN (TA+GM+PB MAV+WL) model estimation (\(R^{2}\): 96.05%, NRMSE: 0.051, RMSE: 0.092 Nm/kg) had better time synchronization with the ID was better than the MLP model, and was able to estimate the dorsiflexion moments at the start and end of the stance phase. However, it also had an offset during the first swing phase and could not estimate the plantar flexor moment peak. Similar to the previous two models, the RCNN (TA+SOL+GM+PB WL) model estimation (\(R^{2}\): 97.61%, NRMSE: 0.039, RMSE: 0.072 Nm/kg) had offset during the first swing phase, and could not estimate the dorsiflexor moments at the start and end of the stance phase.