HOG and Pairwise SVMs for Neuromuscular Activity Recognition Using Instantaneous HD-sEMG Images

The concept of neuromuscular activity recognition using instantaneous high-density surface electromyography (HD-sEMG) image opens up new avenues for the development of more fluid and natural muscle-computer interfaces. The state-of-the-art methods for instantaneous HD-sEMG image recognition achieve prominent performance using a computationally intensive deep convolutional networks (ConvNet) classifier, while very low performance is reported using the conventional classifiers. However, the conventional classifiers such as Support Vector Machines (SVM) can surpass ConvNet at producing optimal classification if well-behaved feature vectors are provided. This paper studies the question of extracting distinctive feature sets, thus propose to use Histograms of Oriented Gradient (HOG) as unique features for robust neuromuscular activity recognition, adopting pair wise SVMs as the classification scheme. The experimental results proved that the HOG represents unique features inside the instantaneous HD-sEMG image and fine-tuning the hyper- parameter of the pair wise SVMs, the recognition accuracy comparable to the more complex state of the art methods can be achieved.