Transfer Learning in Sensor-Based Human Activity Recognition: A Survey

Abstract

Sensor-based human activity recognition (HAR) has been an active research area for many years, resulting in practical applications in smart environments, assisted living, fitness, healthcare, etc. Recently, deep learning based end-to-end training has pushed the state-of-the-art performance in domains such as computer vision and natural language, where large amounts of annotated data are available. However, large quantities of annotated data are typically not available for sensor-based HAR. Moreover, the real-world settings on which HAR is performed differ in terms of sensor modalities, classification tasks, and target users. To address this problem, transfer learning has been explored extensively. In this survey, we focus on these transfer learning methods in the application domains of smart home and wearables-based HAR. In particular, we provide a problem-solution perspective by categorizing and presenting the works in terms of their contributions and the challenges they address. We present an overview of the state-of-the-art for both application domains. Based on our analysis of 246 papers, we highlight the gaps in the literature and provide a roadmap for addressing these. This survey provides a reference to the HAR community, by summarizing the existing works and providing a promising research agenda.