  Despite the vast literature on Human Activity Recognition (HAR) with wearable
inertial sensor data, it is perhaps surprising that there are few studies
investigating semisupervised learning for HAR, particularly in a challenging
scenario with class imbalance problem. In this work, we present a new
benchmark, called A*HAR, towards semisupervised learning for class-imbalanced
HAR. We evaluate state-of-the-art semi-supervised learning method on A*HAR, by
combining Mean Teacher and Convolutional Neural Network. Interestingly, we find
that Mean Teacher boosts the overall performance when training the classifier
with fewer labelled samples and a large amount of unlabeled samples, but the
classifier falls short in handling unbalanced activities. These findings lead
to an interesting open problem, i.e., development of semi-supervised HAR
algorithms that are class-imbalance aware without any prior knowledge on the
class distribution for unlabeled samples. The dataset and benchmark evaluation
are released at https://github.com/I2RDL2/ASTAR-HAR for future research.
