  Semi-supervised learning is crucial for alleviating labelling burdens in
people-centric sensing. However, human-generated data inherently suffer from
distribution shift in semi-supervised learning due to the diverse biological
conditions and behavior patterns of humans. To address this problem, we propose
a generic distributionally robust model for semi-supervised learning on
distributionally shifted data. Considering both the discrepancy and the
consistency between the labeled data and the unlabeled data, we learn the
latent features that reduce person-specific discrepancy and preserve
task-specific consistency. We evaluate our model in a variety of people-centric
recognition tasks on real-world datasets, including intention recognition,
activity recognition, muscular movement recognition and gesture recognition.
The experiment results demonstrate that the proposed model outperforms the
state-of-the-art methods.
