  Machine learning classifiers using surface electromyography are important for
human-machine interfacing and device control. Conventional classifiers such as
support vector machines (SVMs) use manually extracted features based on e.g.
wavelets. These features tend to be fixed and non-person specific, which is a
key limitation due to high person-to-person variability of myography signals.
Deep neural networks, by contrast, can automatically extract person specific
features - an important advantage. However, deep neural networks typically have
the drawback of large numbers of parameters, requiring large training data sets
and powerful hardware not suited to embedded systems. This paper solves these
problems by introducing a compact deep neural network architecture that is much
smaller than existing counterparts. The performance of the compact deep net is
benchmarked against an SVM and compared to other contemporary architectures
across 10 human subjects, comparing Myo and Delsys Trigno electrode sets. The
accuracy of the compact deep net was found to be 84.2 +/- 6% versus 70.5 +/- 7%
for the SVM on the Myo, and 80.3+/- 7% versus 67.8 +/- 9% for the Delsys
system, demonstrating the superior effectiveness of the proposed compact
network, which had just 5,889 parameters - orders of magnitude less than some
contemporary alternatives in this domain while maintaining better performance.
