  Deep learning-based human activity recognition (HAR) methods have shown great
promise in the applications of smart healthcare systems and wireless body
sensor network (BSN). Despite their demonstrated performance in laboratory
settings, the real-world implementation of such methods is still hindered by
the cross-subject issue when adapting to new users. To solve this issue, we
propose ActiveSelfHAR, a framework that combines active learning's benefit of
sparsely acquiring data with actual labels and self- training's benefit of
effectively utilizing unlabeled data to enable the deep model to adapt to the
target domain, i.e., the new users. In this framework, the model trained in the
last iteration or the source domain is first utilized to generate pseudo labels
of the target-domain samples and construct a self-training set based on the
confidence score. Second, we propose to use the spatio-temporal relationships
among the samples in the non-self-training set to augment the core set selected
by active learning. Finally, we combine the self-training set and the augmented
core set to fine-tune the model. We demonstrate our method by comparing it with
state-of-the-art methods on two IMU-based datasets and an EMG-based dataset.
Our method presents similar HAR accuracies with the upper bound, i.e. fully
supervised fine-tuning with less than 1\% labeled data of the target dataset
and significantly improves data efficiency and time cost. Our work highlights
the potential of implementing user-independent HAR methods into smart
healthcare systems and BSN.
