  The research in myoelectric control systems primarily focuses on extracting
discriminative representations from the electromyographic (EMG) signal by
designing handcrafted features. Recently, deep learning techniques have been
applied to the challenging task of EMG-based gesture recognition. The adoption
of these techniques slowly shifts the focus from feature engineering to feature
learning. However, the black-box nature of deep learning makes it hard to
understand the type of information learned by the network and how it relates to
handcrafted features. Additionally, due to the high variability in EMG
recordings between participants, deep features tend to generalize poorly across
subjects using standard training methods. Consequently, this work introduces a
new multi-domain learning algorithm, named ADANN, which significantly enhances
(p=0.00004) inter-subject classification accuracy by an average of 19.40%
compared to standard training. Using ADANN-generated features, the main
contribution of this work is to provide the first topological data analysis of
EMG-based gesture recognition for the characterisation of the information
encoded within a deep network, using handcrafted features as landmarks. This
analysis reveals that handcrafted features and the learned features (in the
earlier layers) both try to discriminate between all gestures, but do not
encode the same information to do so. Furthermore, using convolutional network
visualization techniques reveal that learned features tend to ignore the most
activated channel during gesture contraction, which is in stark contrast with
the prevalence of handcrafted features designed to capture amplitude
information. Overall, this work paves the way for hybrid feature sets by
providing a clear guideline of complementary information encoded within learned
and handcrafted features.
