  Objective: The objective of the study is to efficiently increase the
expressivity of surface electromyography-based (sEMG) gesture recognition
systems. Approach: We use a problem transformation approach, in which actions
were subset into two biomechanically independent components - a set of wrist
directions and a set of finger modifiers. To maintain fast calibration time, we
train models for each component using only individual gestures, and extrapolate
to the full product space of combination gestures by generating synthetic data.
We collected a supervised dataset with high-confidence ground truth labels in
which subjects performed combination gestures while holding a joystick, and
conducted experiments to analyze the impact of model architectures, classifier
algorithms, and synthetic data generation strategies on the performance of the
proposed approach. Main Results: We found that a problem transformation
approach using a parallel model architecture in combination with a non-linear
classifier, along with restricted synthetic data generation, shows promise in
increasing the expressivity of sEMG-based gestures with a short calibration
time. Significance: sEMG-based gesture recognition has applications in
human-computer interaction, virtual reality, and the control of robotic and
prosthetic devices. Existing approaches require exhaustive model calibration.
The proposed approach increases expressivity without requiring users to
demonstrate all combination gesture classes. Our results may be extended to
larger gesture vocabularies and more complicated model architectures.
