Surface EMG-Based Intersession/Intersubject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning

Gesture recognition using low-resolution instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the development of more fluid and natural muscle–computer interfaces (MCIs). However, the data variability between intersession and intersubject scenarios presents a great challenge. The existing approaches employed very large and complex deep ConvNet or two-stage recurrent neural network (2SRNN)-based domain adaptation methods to approximate the distribution shift caused by these intersession and intersubject data variabilities. Hence, these methods also require learning over millions of training parameters and a large pretrained and target domain dataset in both the pretraining and the adaptation stages. As a result, it makes high-end resource-bounded and computationally very expensive network for deployment in real-time applications. To overcome this problem, we propose a lightweight All-ConvNet + transfer learning (TL) model that leverages lightweight All-ConvNet and TL for the enhancement of intersession and intersubject 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 intersession and intersubject 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 intersession and intersubject scenarios and perform on par with or competitively on intrasession 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 intersession and intersubject gesture recognition tasks.