The recognition of dynamic gestures via discrete myoelectric control is becoming an attractive input modality for event-based human-machine interaction. While recent work suggests that discrete cross-user models are possible (i.e., no calibration data is required from the end user), few have evaluated techniques to optimize these systems and their generalizability to new randomly prompted data simulating real-world discrete control is relatively unknown. Using the 612-user EMG-EPN dataset, this work benchmarks five temporal deep-learning approaches, evaluates the tradeoff between performance and model complexity, and compares hand-crafted and machine-learned feature extraction techniques. The CNN-GRU achieved the best accuracy of 93% when tested on 184 previously unseen users. These models were then evaluated on a different, more realistic, unsegmented dataset, wherein users were randomly prompted to elicit discrete gestures during a continuous data collection. This dataset was recorded five years apart and in a separate country from the data used to train the original model. Results show that the cross-user results scale to this new dataset (accuracies of 96%) even though no feedback was given to users during the elicitation of gestures. These findings highlight the exciting potential of discrete zero-shot cross-user myoelectric control and offer valuable insights for the continued advancement of this promising technology.

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