Transfer Learning for Activity Recognition: A Survey

Diane Cook; Kyle D Feuz; Narayanan C Krishnan

doi:10.1007/s10115-013-0665-3

Back

Transfer Learning for Activity Recognition: A Survey

Journal article

Open access

Peer reviewed

Transfer Learning for Activity Recognition: A Survey

Diane Cook, Kyle D Feuz and Narayanan C Krishnan

Knowledge and information systems, Vol.36(3), pp.537-556

09/01/2013

DOI: https://doi.org/10.1007/s10115-013-0665-3

Handle:

https://hdl.handle.net/2376/114960

PMCID: PMC3768027

PMID: 24039326

Files and links (1)

url

https://doi.org/10.1007/s10115-013-0665-3View

Published (Version of record) Open

Abstract

Activity Recognition

Transfer Learning

Machine Learning

Smart Environments

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 perform transfer-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.

Metrics

7 Record Views

See more details

Details

Title: Transfer Learning for Activity Recognition: A Survey
Creators: Diane Cook - Department of Electrical Engineering and Computer Science, Washington State University, Pullman WA, USA
Kyle D Feuz - Department of Electrical Engineering and Computer Science, Washington State University, Pullman WA, USA
Narayanan C Krishnan - Department of Electrical Engineering and Computer Science, Washington State University, Pullman WA, USA
Publication Details: Knowledge and information systems, Vol.36(3), pp.537-556
Academic Unit: Electrical Engineering and Computer Science, School of
Grant note: R01 EB015853 || EB / National Institute of Biomedical Imaging and Bioengineering : NIBIB R01 EB009675 || EB / National Institute of Biomedical Imaging and Bioengineering : NIBIB
Identifiers: 99900548021301842
Language: English
Resource Type: Journal article