###### Abstract  
The joint moment is a key measurement in locomotion analysis. Transferable prediction across different subjects is advantageous for calibration-free, practical clinical applications. However, even for similar gait motions, intersubject variance presents a significant challenge in maintaining reliable prediction performance. The optimal deep learning models for ankle moment prediction during dynamic gait motions remain undeexplored for both intrasubject and intersubject usage. This study evaluates the feasibility of different deep-learning models for estimating ankle moments using sEMG data to find an optimal intrasubject model against the inverse dynamic approach. We verified and compared the performance of 1302 intrasubject models per subject on 597 steps from seven subjects using various architectures and feature sets. The best-performing intrasubject models were recurrent convolutional neural networks trained using signal energy features. They were then transferred to realize intersubject ankle moment estimation. deep learning; recurrent CNN; sEMG features; transferable estimation; gait analysis +
Footnote †: journal: Medical  
In data-driven models, extracting meaningful information related to the task is vital. [PERSON] et al. [9] have shown that using featureless sEMG signals results in lower performance compared to using sEMG features. A key question is: which muscles and features contribute most to the training of an efficient model? [PERSON] et al. [10] studied the properties of 37 time and frequency domain sEMG features calculated using sEMG signals for building a hand gesture classification model. They classified the time domain features into four groups based on their mathematical properties: (1) energy and complexity information methods, (2) frequency information methods, (3) prediction model methods, and (4) time-dependence methods. The features from the first group--such as mean absolute value (MAV)--and the second group--such as Zero crossing count (ZC)--performed better than those from the last group and frequency domain. Although the study by Ref. [10] was for hand classification, the feature extraction methods and categorization can be applied to lower limb sEMG signals as well. [PERSON] et al. [5] trained the Elman neural network model for ankle moment estimation by extracting the root mean square (RMS) features from four muscles' sEMG signal (biceps femoris (BF), semitendinosus (ST), vastus lateralis (VL), and gastrocnemius) as well as thigh and shank angles. Their intrasubject model achieved a normalized root mean square error (NRMSE) of \(11.6\%\pm 0.45\%\). [PERSON] et al. [8] trained multi-layer perception (MLP) models using three shank muscles (tibialis anterior (TA), soleus (SOL), and gastrocnemius medialis (GM)) along with the ankle angle for estimating the ankle moment. They trained different models based on different data collection methods and validated each model across seven different activities. They then compared the MLP results with an NMS model. The study found that NMS models can outperform MLP models when the sEMG signal reaches maximum levels; however, the MLP model performed better when trained on a large and varied set of trials. Their best walking model achieved a root mean square error (RMSE) of \(0.01\pm 0.01\) Nm/kg, but it was trained and tested on data with a fixed walking speed. When the volunteers' walking speed was not determined, the best MLP model achieved an RMSE of \(0.2\pm 0.1\) Nm/kg, showing that training a model with a determined walking speed is easier than with free walking speed. [PERSON] et al. [11] compared the performance of MLP, neural ordinary differential equations (ODE), convolutional neural networks (CNN), and long short-term memory (LSTM) models with 30,642, 30,912, 156,264, and 32,306 parameters, respectively, for both legs' ankle moment estimation. They used four sEMG sensors on each leg (TA, GM, ST, and vastus medialis (VM)), each equipped with accelerometers to provide additional input to the model.