  There is a growing body of studies on applying deep learning to biometrics
analysis. Certain circumstances, however, could impair the objective measures
and accuracy of the proposed biometric data analysis methods. For instance,
people with chronic pain (CP) unconsciously adapt specific body movements to
protect themselves from injury or additional pain. Because there is no
dedicated benchmark database to analyse this correlation, we considered one of
the specific circumstances that potentially influence a person's biometrics
during daily activities in this study and classified pain level and
pain-related behaviour in the EmoPain database. To achieve this, we proposed a
sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated
recurrent unit (GRU) that incorporates multiple autoencoders using a shared
training framework. This architecture is fed by multidimensional data collected
from inertial measurement unit (IMU) and surface electromyography (sEMG)
sensors. Furthermore, to compensate for variations in the temporal dimension
that may not be perfectly represented in the latent space of s-RNNs, we fused
hand-crafted features derived from information-theoretic approaches with
represented features in the shared hidden state. We conducted several
experiments which indicate that the proposed method outperforms the
state-of-the-art approaches in classifying both pain level and pain-related
behaviour.
