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.
@article{arxiv.2101.04859,
title = {A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition},
author = {Govind Narasimman and Kangkang Lu and Arun Raja and Chuan Sheng Foo and Mohamed Sabry Aly and Jie Lin and Vijay Chandrasekhar},
journal= {arXiv preprint arXiv:2101.04859},
year = {2021}
}