Advancements in EMG pattern recognition have enabled intuitive gesture recognition of upper limb motions for use in human computer interfaces. Whether it be to control a prosthesis, a drone, or a virtual reality environment, all current implementations rely on the acquisition of representative training data from the end-user before use. This requirement has been reported as frustrating for users and a limitation to the broader adoption of EMG-based controllers. Consequently, this thesis aims to address the burden of the training protocol for EMG pattern recognition using data-driven approaches and cross-user models. Two exploratory studies were conducted to assess the impact and degree of inter-subject variability and difference in EMG information content between user groups. Based on observed subject differences, an adaptive domain adversarial neural network (ADANN) was subsequently developed to adapt a previously trained model to a new user using minimal training data. The proposed ADANN cross-subject model significantly outperformed the current state-of-the-art canonical correlation analysis (CCA) cross-subject model for both intact-limb and amputee populations (with 9.4% and 22.4% absolute improvement, respectively). Finally, a generative adversarial network architecture, SinGAN, was adopted as a novel alternative for reducing the amount of EMG data needed for training. SinGAN was able to generate synthetic EMG signals based on a single subject-supplied motion repetition, significantly improving accuracy compared to training with the single motion alone.
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