article

Transfer learning for activity recognition: a survey

Authors:

Narayanan C. KrishnanAuthors Info & Claims

Knowledge and Information Systems, Volume 36, Issue 3

Pages 537 - 556

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

Published: 01 September 2013 Publication History

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 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.

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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 36, Issue 3

September 2013

283 pages

ISSN:0219-1377

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Copyright © Copyright © 2013 Springer-Verlag London.

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Published: 01 September 2013

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Kou HShi HZhao H(2024)Subspace and second-order statistical distribution alignment for cross-domain recognition of human hand motionsJournal of Intelligent Manufacturing10.1007/s10845-023-02150-z35:5(2277-2293)Online publication date: 1-Jun-2024
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