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.

本研究提出了 ActiveHARNet，一种支持设备上增量学习和推理的资源有效的深度集成模型，该模型使用 dropout 中的 Bayesian 神经网络近似来表示模型不确定性，并结合适当的主动学习获取函数。在两个公开可用的手腕 HAR 和跌倒检测数据集上的实证结果表明，ActiveHARNet 在不同用户之间的推断效率上实现了显着的提升，并在增量学习中获得了至少 60％的数据采集减少率，从而证明了部署和增量学习的可行性。