论文标题
ACTILABEL：活动识别的组合转移学习框架
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition
论文作者
论文摘要
基于传感器的人类活动识别已成为许多新兴应用的关键组成部分，从行为医学到游戏。然而，在贸易Internet Internet时代，传感器设备多样性的前所未有的增加限制了采用活动识别模型以跨不同领域使用。我们建议ActiLabel一个组合框架，该框架了解任意领域和不同领域的事件之间的结构相似性。结构相似性通过图模型捕获，称为IT依赖关系图，该图在低级信号和特征空间中抽象了活动模式的详细信息。然后，通过在依赖关系图之间找到最佳的分层映射来自主学习。基于三个公共数据集的广泛实验证明了Actilabel比最先进的转移学习和深度学习方法的优越性。
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.
