Cross-domain Activity Recognition via Substructural Optimal Transport

It is expensive and time-consuming to collect sufficient labeled data for human activity recognition (HAR). Recently, lots of work solves the problem via domain adaptation which leverages the labeled samples from the source domain to annotate the target domain. Existing domain adaptation methods mainly focus on adapting cross-domain representations via domain-level, class-level, or sample-level distribution matching. However, the domain- and class-level matching are too coarse that may result in under-adaptation, while sample-level matching may be affected by the noise seriously and eventually cause over-adaptation. In this paper, we propose substructure-level matching for domain adaptation (SSDA) to utilize the internal substructures of the domain to perform accurate and efficient knowledge transfer. Based on SSDA, we propose an optimal transport-based implementation, Substructural Optimal Transport (SOT), for cross-domain HAR. We obtain the substructures of activities via clustering methods and seeks the coupling of the weighted substructures between different domains. We conduct comprehensive experiments on four large public activity recognition datasets (i.e. UCI-DSADS, UCI-HAR, USC-HAD, PAMAP2), which demonstrates that SOT significantly outperforms other state-of-the-art methods w.r.t classification accuracy (10%+ improvement). In addition, SOT is much faster than comparison methods.