  Various health-care applications such as assisted living, fall detection
etc., require modeling of user behavior through Human Activity Recognition
(HAR). HAR using mobile- and wearable-based deep learning algorithms have been
on the rise owing to the advancements in pervasive computing. However, there
are two other challenges that need to be addressed: first, the deep learning
model should support on-device incremental training (model updation) from
real-time incoming data points to learn user behavior over time, while also
being resource-friendly; second, a suitable ground truthing technique (like
Active Learning) should help establish labels on-the-fly while also selecting
only the most informative data points to query from an oracle. Hence, in this
paper, we propose ActiveHARNet, a resource-efficient deep ensembled model which
supports on-device Incremental Learning and inference, with capabilities to
represent model uncertainties through approximations in Bayesian Neural
Networks using dropout. This is combined with suitable acquisition functions
for active learning. Empirical results on two publicly available wrist-worn HAR
and fall detection datasets indicate that ActiveHARNet achieves considerable
efficiency boost during inference across different users, with a substantially
low number of acquired pool points (at least 60% reduction) during incremental
learning on both datasets experimented with various acquisition functions, thus
demonstrating deployment and Incremental Learning feasibility.
