  Human Activity Recognition (HAR) based on the sensors of mobile/wearable
devices aims to detect the physical activities performed by humans in their
daily lives. Although supervised learning methods are the most effective in
this task, their effectiveness is constrained to using a large amount of
labeled data during training. While collecting raw unlabeled data can be
relatively easy, annotating data is challenging due to costs, intrusiveness,
and time constraints.
  To address these challenges, this paper explores alternative approaches for
accurate HAR using a limited amount of labeled data. In particular, we have
adapted recent Self-Supervised Learning (SSL) algorithms to the HAR domain and
compared their effectiveness. We investigate three state-of-the-art SSL
techniques of different families: contrastive, generative, and predictive.
Additionally, we evaluate the impact of the underlying neural network on the
recognition rate by comparing state-of-the-art CNN and transformer
architectures.
  Our results show that a Masked Auto Encoder (MAE) approach significantly
outperforms other SSL approaches, including SimCLR, commonly considered one of
the best-performing SSL methods in the HAR domain.
  The code and the pre-trained SSL models are publicly available for further
research and development.
