Sensor-based human activity recognition has become a critical component of many emerging applications ranging from behavioral medicine to gaming. However, an unprecedented increase in the diversity of sensor devices in the Internet-of-Things era has limited the adoption of activity recognition models for use across different domains. We propose ActiLabel a combinatorial framework that learns structural similarities among the events in an arbitrary domain and those of a different domain. The structural similarities are captured through a graph model, referred to as the it dependency graph, which abstracts details of activity patterns in low-level signal and feature space. The activity labels are then autonomously learned by finding an optimal tiered mapping between the dependency graphs. Extensive experiments based on three public datasets demonstrate the superiority of ActiLabel over state-of-the-art transfer learning and deep learning methods.

中文翻译：

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ActiLabel：用于活动识别的组合迁移学习框架

基于传感器的人类活动识别已成为从行为医学到游戏等许多新兴应用的重要组成部分。然而，物联网时代传感器设备多样性的空前增加限制了活动识别模型在不同领域的采用。我们提出 ActiLabel 一个组合框架，可以学习任意域和不同域中事件之间的结构相似性。结构相似性是通过图模型捕获的，称为 it 依赖图，它抽象了低级信号和特征空间中活动模式的细节。然后通过找到依赖图之间的最佳分层映射来自主学习活动标签。