  Machine learning and deep learning have shown great promise in mobile sensing
applications, including Human Activity Recognition. However, the performance of
such models in real-world settings largely depends on the availability of large
datasets that captures diverse behaviors. Recently, studies in computer vision
and natural language processing have shown that leveraging massive amounts of
unlabeled data enables performance on par with state-of-the-art supervised
models.
  In this work, we present SelfHAR, a semi-supervised model that effectively
learns to leverage unlabeled mobile sensing datasets to complement small
labeled datasets. Our approach combines teacher-student self-training, which
distills the knowledge of unlabeled and labeled datasets while allowing for
data augmentation, and multi-task self-supervision, which learns robust
signal-level representations by predicting distorted versions of the input.
  We evaluated SelfHAR on various HAR datasets and showed state-of-the-art
performance over supervised and previous semi-supervised approaches, with up to
12% increase in F1 score using the same number of model parameters at
inference. Furthermore, SelfHAR is data-efficient, reaching similar performance
using up to 10 times less labeled data compared to supervised approaches. Our
work not only achieves state-of-the-art performance in a diverse set of HAR
datasets, but also sheds light on how pre-training tasks may affect downstream
performance.
