  Supervised Deep Learning (DL) models are currently the leading approach for
sensor-based Human Activity Recognition (HAR) on wearable and mobile devices.
However, training them requires large amounts of labeled data whose collection
is often time-consuming, expensive, and error-prone. At the same time, due to
the intra- and inter-variability of activity execution, activity models should
be personalized for each user. In this work, we propose SelfAct: a novel
framework for HAR combining self-supervised and active learning to mitigate
these problems. SelfAct leverages a large pool of unlabeled data collected from
many users to pre-train through self-supervision a DL model, with the goal of
learning a meaningful and efficient latent representation of sensor data. The
resulting pre-trained model can be locally used by new users, which will
fine-tune it thanks to a novel unsupervised active learning strategy. Our
experiments on two publicly available HAR datasets demonstrate that SelfAct
achieves results that are close to or even better than the ones of fully
supervised approaches with a small number of active learning queries.
