Human activity recognition (HAR) is a time series classification task that
focuses on identifying the motion patterns from human sensor readings. Adequate
data is essential but a major bottleneck for training a generalizable HAR
model, which assists customization and optimization of online web applications.
However, it is costly in time and economy to collect large-scale labeled data
in reality, i.e., the low-resource challenge. Meanwhile, data collected from
different persons have distribution shifts due to different living habits, body
shapes, age groups, etc. The low-resource and distribution shift challenges are
detrimental to HAR when applying the trained model to new unseen subjects. In
this paper, we propose a novel approach called Diverse and Discriminative
representation Learning (DDLearn) for generalizable low-resource HAR. DDLearn
simultaneously considers diversity and discrimination learning. With the
constructed self-supervised learning task, DDLearn enlarges the data diversity
and explores the latent activity properties. Then, we propose a diversity
preservation module to preserve the diversity of learned features by enlarging
the distribution divergence between the original and augmented domains.
Meanwhile, DDLearn also enhances semantic discrimination by learning
discriminative representations with supervised contrastive learning. Extensive
experiments on three public HAR datasets demonstrate that our method
significantly outperforms state-of-art methods by an average accuracy
improvement of 9.5% under the low-resource distribution shift scenarios, while
being a generic, explainable, and flexible framework.