3 research outputs found
Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions
Transfer Learning in Human Activity Recognition: A Survey
Sensor-based human activity recognition (HAR) has been an active research
area, owing to its applications in smart environments, assisted living,
fitness, healthcare, etc. Recently, deep learning based end-to-end training has
resulted in 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 not available for sensor-based HAR.
Moreover, the real-world settings on which the HAR is performed differ in terms
of sensor modalities, classification tasks, and target users. To address this
problem, transfer learning has been employed 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 also present an updated view
of the state-of-the-art for both application domains. Based on our analysis of
205 papers, we highlight the gaps in the literature and provide a roadmap for
addressing them. This survey provides a reference to the HAR community, by
summarizing the existing works and providing a promising research agenda.Comment: 40 pages, 5 figures, 7 table