  Wearable sensor-based human activity recognition (HAR) has been a research
focus in the field of ubiquitous and mobile computing for years. In recent
years, many deep models have been applied to HAR problems. However, deep
learning methods typically require a large amount of data for models to
generalize well. Significant variances caused by different participants or
diverse sensor devices limit the direct application of a pre-trained model to a
subject or device that has not been seen before. To address these problems, we
present an invariant feature learning framework (IFLF) that extracts common
information shared across subjects and devices. IFLF incorporates two learning
paradigms: 1) meta-learning to capture robust features across seen domains and
adapt to an unseen one with similarity-based data selection; 2) multi-task
learning to deal with data shortage and enhance overall performance via
knowledge sharing among different subjects. Experiments demonstrated that IFLF
is effective in handling both subject and device diversion across popular open
datasets and an in-house dataset. It outperforms a baseline model of up to 40%
in test accuracy.
