  Hand gesture recognition (HGR) has gained significant attention due to the
increasing use of AI-powered human-computer interfaces that can interpret the
deep spatiotemporal dynamics of biosignals from the peripheral nervous system,
such as surface electromyography (sEMG). These interfaces have a range of
applications, including the control of extended reality, agile prosthetics, and
exoskeletons. However, the natural variability of sEMG among individuals has
led researchers to focus on subject-specific solutions. Deep learning methods,
which often have complex structures, are particularly data-hungry and can be
time-consuming to train, making them less practical for subject-specific
applications. In this paper, we propose and develop a generalizable, sequential
decoder of transient high-density sEMG (HD-sEMG) that achieves 73% average
accuracy on 65 gestures for partially-observed subjects through
subject-embedded transfer learning, leveraging pre-knowledge of HGR acquired
during pre-training. The use of transient HD-sEMG before gesture stabilization
allows us to predict gestures with the ultimate goal of counterbalancing system
control delays. The results show that the proposed generalized models
significantly outperform subject-specific approaches, especially when the
training data is limited, and there is a significant number of gesture classes.
By building on pre-knowledge and incorporating a multiplicative
subject-embedded structure, our method comparatively achieves more than 13%
average accuracy across partially observed subjects with minimal data
availability. This work highlights the potential of HD-sEMG and demonstrates
the benefits of modeling common patterns across users to reduce the need for
large amounts of data for new users, enhancing practicality.
