  Gesture recognition using low-resolution instantaneous HD-sEMG images opens
up new avenues for the development of more fluid and natural muscle-computer
interfaces. However, the data variability between inter-session and
inter-subject scenarios presents a great challenge. The existing approaches
employed very large and complex deep ConvNet or 2SRNN-based domain adaptation
methods to approximate the distribution shift caused by these inter-session and
inter-subject data variability. Hence, these methods also require learning over
millions of training parameters and a large pre-trained and target domain
dataset in both the pre-training and adaptation stages. As a result, it makes
high-end resource-bounded and computationally very expensive for deployment in
real-time applications. To overcome this problem, we propose a lightweight
All-ConvNet+TL model that leverages lightweight All-ConvNet and transfer
learning (TL) for the enhancement of inter-session and inter-subject gesture
recognition performance. The All-ConvNet+TL model consists solely of
convolutional layers, a simple yet efficient framework for learning invariant
and discriminative representations to address the distribution shifts caused by
inter-session and inter-subject data variability. Experiments on four datasets
demonstrate that our proposed methods outperform the most complex existing
approaches by a large margin and achieve state-of-the-art results on
inter-session and inter-subject scenarios and perform on par or competitively
on intra-session gesture recognition. These performance gaps increase even more
when a tiny amount (e.g., a single trial) of data is available on the target
domain for adaptation. These outstanding experimental results provide evidence
that the current state-of-the-art models may be overparameterized for
sEMG-based inter-session and inter-subject gesture recognition tasks.
