“…In addition, as mentioned by Saxby et al (2020), the processing of US imaging data to derive the aforementioned explicit features is relatively subjective, time-consuming, and tedious. Therefore, more advanced deep learning approaches have been developed to deal with the high-dimensional explicit and/or implicit features from either sEMG or US signals for continuous joint kinematics and/or kinetics estimation (indicating human motion intent or volitional effort) (Cunningham et al, 2017b;Liu et al, 2019;Cunningham and Loram, 2020;Ma et al, 2020;Xu et al, 2020;Yu et al, 2020;Pancholi et al, 2021). For example, in Xu et al (2020), parallel convolutional neural networks (CNNs) were used to continuously predict an individual finger's force in real-time based on the temporal energy heatmap and frequency spectrum map from high-density sEMG signals, which outperformed the prediction performance by using a motor unit decomposition method and a conventional sEMG amplitude-based method.…”