Semi-supervised Multi-source Domain Adaptation in Wearable Activity Recognition

Abstract

The scarcity of labeled data has traditionally been the primary hindrance in building scalable supervised deep learning models that can retain adequate performance in the presence of various heterogeneities in sample distributions. Domain adaptation tries to address this issue by adapting features learned from a smaller set of labeled samples to that of the incoming unlabeled samples. The traditional domain adaptation approaches normally consider only a single source of labeled samples, but in real world use cases, labeled samples can originate from multiple-sourcesproviding motivation for multi-source domain adaptation (MSDA). Several MSDA approaches have been investigated for wearable sensor-based human activity recognition (HAR) in recent times, but their performance improvement compared to single source counterpart remained marginal. To remedy this performance gap that, we explore multiple avenues to align the conditional distributions in addition to the usual alignment of marginal ones. In our investigation, we extend an existing multi-source domain adaptation approach under semi-supervised settings. We assume the availability of partially labeled target domain data and further explore the pseudo labeling usage with a goal to achieve a performance similar to the former. In our experiments on three publicly available datasets, we find that a limited labeled target domain data and pseudo label data boost the performance over the unsupervised approach by 10-35% and 2-6%, respectively, in various domain adaptation scenarios.