Elgayar, Doctor NeamatTaylor, Professor Nicholas KenelmTurcanu, Doctor AdrianElMohandes, Hend2026-02-132025-10https://www.ros.hw.ac.uk/handle/10399/5300The advancement of machine learning (ML) offers significant potential for improving assistive technologies aimed at restoring function for individuals with upper limb loss. Although ML techniques such as direct control and pattern recognition have been extensively explored over the past decades, current commercial myoelectric prostheses remain limited in functionality. They typically support a small number of movements, lack simultaneous control, and suffer from low reliability due to issues like electrode displacement and physiological variability, which necessitate frequent recalibration. Recent research has therefore shifted toward deep learning (DL) and regression-based approaches capable of decoding continuous joint kinematics. However, challenges persist in achieving robust interpretation of user intent across multiple degrees of freedom (DOFs), particularly in scenarios involving unseen movements or cross-subject deployment. Few studies have systematically examined these conditions, despite their importance for clinical translation. This thesis addresses these gaps by providing the first large-scale evaluation of deep regression models for sEMG-based multi-DOF decoding under both within-subject and cross-subject conditions. Specifically, three contributions are made: (1) an extensive analysis of CNN-LSTM architectures for simultaneous and proportional joint control across 22 DOFs, tested on a broader and more diverse dataset than in prior studies; (2) a systematic investigation of model robustness under unseen movements and session variability, demonstrating improved generalization through multi-scale temporal feature representations; and (3) a novel cross-subject framework that integrates model-agnostic meta-learning (MAML), ensemble learning, and multihead decoding, yielding substantial improvements under both few-shot and zero-shot conditions. Our findings show that deep learning models, when combined with principled generalization strategies, can outperform traditional approaches in accuracy, robustness, and scalability, moving myoelectric prosthesis control closer to practical real-world use.enDoctor of Philosophy