title: Transfer Learning for Activity Recognition: A Survey.
abstract: 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 performtransfer-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.
authors.affiliation: Department of Electrical Engineering and Computer Science, Washington State University, Pullman WA, USA.
authors.forename: Diane
authors.initials: D
authors.lastname: Cook
authors.forename: Kyle D
authors.initials: KD
authors.lastname: Feuz
authors.forename: Narayanan C
authors.initials: NC
authors.lastname: Krishnan
keywords: Activity Recognition; Machine Learning; Smart Environments; Transfer Learning
references.citation: IEEE Trans Knowl Data Eng. 2011;23(4):527-539
references.pmid: 21617742
references.citation: IEEE Pervasive Comput. 2010;9(1):48
references.pmid: 21258659
references.citation: Psychol Bull. 2002 Jul;128(4):612-37
references.pmid: 12081085
references.citation: Comput Methods Programs Biomed. 2008 Jul;91(1):55-81
references.pmid: 18367286
references.citation: Science. 2000 Dec 22;290(5500):2319-23
references.pmid: 11125149
references.citation: IEEE Trans Neural Netw. 2011 Feb;22(2):199-210
references.pmid: 21095864
references.citation: IEEE Intell Syst. 2010 Sep 9;2010(99):1
references.pmid: 21461133
references.citation: IEEE Trans Pattern Anal Mach Intell. 2012 Sep;34(9):1667-80
references.pmid: 22201057