###### Abstract
The LSTM model achieved the best results during walking trials, with an RMSE of \(0.07\pm 0.02\) Nm/kg. In our previous study [12], we trained LSTM models to estimate knee and ankle angles and moments from both legs simultaneously, using seven sEMG signals on each leg (rectus femoris (RF), BF, VL, ST, TA, GM, and SOL) and extracted eight features per muscle. The models were trained and tested in three daily activities: (1) squat, (2) picking an object from the ground and putting it back, and (3) sitting on a chair and standing up and achieved average coefficient of determination (\(R^{2}\)) \(97.25\%\pm 1.46\%\) and \(94.9\%\pm 1.4\%\), respectively, for the knee angle and moment estimation, but found some difficulties with the ankle angle and moment (\(R^{2}\) average: \(91.44\%\pm 3.48\%\) and \(85.44\%\pm 8.14\%\), respectively). In that study, the gait was not considered. Beyond the regression domain, [PERSON] et al. [13] trained classification models to classify upstairs, downstairs, uphill, and downhill as well as the transition of all these tasks from and to flat ground and the flat ground classification from sEMG signals. They recorded the sEMG signals from eleven muscles (VL, VM, RF, BF, ST, TA, SOL, gastrocnemius lateral (GL), flexor hallucis longus (FHL), and extensor digitorum longus (EDL)) and experimented with 33 different muscle combinations. Their best model, using all eleven muscles, achieved a classification accuracy of \(95.4\%\). Although [13] their study was for a classification task, it raised the question of whether similar results can be obtained for regression tasks. Beyond joint kinetics, sEMG signals have also been used with deep learning models, such as LSTM, to estimate ground reaction forces [14]. sEMG signals reflect muscle activation levels and, consequently, joint moments. However, activation patterns can vary between individuals and are influenced by sensor placement. Inertial measurement unit (IMU) sensors, though lacking direct muscle kinetics information, are less sensitive to these factors and benefit from the repetitive nature of gait cycles and the relationship between joint angles and moments. [PERSON] et al. [6] conducted a comparative intersubject study of MLP and LSTM models trained on simulated IMU. Their LSTM model outperformed the MLP model, achieving a lower NRMSE of 7.32% compared to the MLP's 7.39%. [PERSON] et al. [15] used the subject's anthropometrics and kinematics information from a motion capture system in a CNN model and achieved an \(R^{2}\) of \(92\%\pm 3\%\). In [5; 8; 12], a single model architecture was employed, and features, as well as muscle selection, were determined in advance. Conversely, refs. [6; 11] employed multiple model architectures but with determined features and muscle selection. Previous research has not comprehensively justified the combination of model architecture, muscles, and feature selection. Finding a transferable model presents a challenging problem and requires researching different models and techniques [16; 17]. While previous studies have explored feature selection [10], muscle selection [13], or model selection [6; 11], as far as we know, a comprehensive analysis encompassing all three aspects has been lacking. Additionally, our previous LSTM model [12] had difficulties estimating the ankle moment. This study aimed to bridge this gap by empirically analyzing the architecture of three distinct DL models, in conjunction with selecting muscles and extracted features. The goal is to build a transferable and generalized ankle moment estimation model during normal gait using sEMG sensors, without including kinematics or anthropometric information.