It is expensive and time-consuming to collect sufficient labeled data for human activity recognition (HAR). Domain adaptation is a promising approach for cross-domain activity recognition. Existing methods mainly focus on adapting cross-domain representations via domain-level, class-level, or sample-level distribution matching. However, they might fail to capture the fine-grained locality information in activity data. 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 better utilize the locality information of activity data for 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 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 (9%+ improvement). In addition, SOT is $\mathbf{5}\times$ faster than traditional OT-based DA methods with the same hyper-parameters.

收集足够的标记数据以进行人类活动识别（HAR）既昂贵又耗时。域自适应是一种用于跨域活动识别的有前途的方法。现有方法主要集中在通过域级别，类级别或样本级别的分布匹配来适应跨域表示。但是，他们可能无法捕获活动数据中的细粒度位置信息。域和类级别的匹配过于粗糙，可能导致自适应不足，而样本级别的匹配可能会受到噪声的严重影响，最终导致过度自适应。在本文中，我们提出了用于领域适应的子结构级匹配（SSDA），以更好地利用活动数据的位置信息来进行准确而有效的知识转移。基于SSDA，我们为跨域HAR提出了一种基于最佳传输的实现，即子结构最佳传输（SOT）。我们通过聚类方法获得活动的子结构，并寻求不同域之间加权子结构的耦合。我们对四个公共活动识别数据集（即UCI-DSADS，UCI-HAR，USC-HAD，PAMAP2）进行了综合实验，证明了SOT在分类准确度方面明显优于其他最新方法（提升9％以上）。另外，SOT是$\mathbf{5}\times$ 与具有相同超参数的传统基于OT的DA方法相比，速度更快。