  In the recent past, psychological stress has been increasingly observed in
humans, and early detection is crucial to prevent health risks. Stress
detection using on-device deep learning algorithms has been on the rise owing
to advancements in pervasive computing. However, an important challenge that
needs to be addressed is handling unlabeled data in real-time via suitable
ground truthing techniques (like Active Learning), which should help establish
affective states (labels) while also selecting only the most informative data
points to query from an oracle. In this paper, we propose a framework with
capabilities to represent model uncertainties through approximations in
Bayesian Neural Networks using Monte-Carlo (MC) Dropout. This is combined with
suitable acquisition functions for active learning. Empirical results on a
popular stress and affect detection dataset experimented on a Raspberry Pi 2
indicate that our proposed framework achieves a considerable efficiency boost
during inference, with a substantially low number of acquired pool points
during active learning across various acquisition functions. Variation Ratios
achieves an accuracy of 90.38% which is comparable to the maximum test accuracy
achieved while training on about 40% lesser data.
