Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper, we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed.

中文翻译：

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活动识别的迁移学习：一项调查。

许多关注人类需求的智能系统需要有关人类正在执行的活动的信息。此功能的核心是活动识别，这是一个具有挑战性且经过深入研究的问题。活动识别算法需要大量标记的训练数据，但需要在非常多样化的情况下表现良好。因此，研究人员一直在设计方法来识别和利用活动识别数据集之间的微妙联系，或执行基于转移的活动识别. 在本文中，我们调查了文献以强调迁移学习在活动识别方面的最新进展。我们通过传感器模式、源环境和目标环境之间的差异、数据可用性以及传输的信息类型来描述基于传输的活动识别的现有方法。最后，随着该领域的进一步发展，我们提出了一些重大挑战供社区考虑。