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Unsupervised Domain Adaptation for Human Activity Recognition

  • Paulo Barbosa
  • Kemilly Dearo Garcia
  • João Mendes-MoreiraEmail author
  • André C. P. L. F. de Carvalho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11314)

Abstract

Human Activity Recognition has been primarily investigated as a machine learning classification task forcing it to handle with two main limitations. First, it must assume that the testing data has an equal distribution with the training sample. However, the inherent structure of an activity recognition systems is fertile in distribution changes over time, for instance, a specific person can perform physical activities differently from others, and even sensors are prone to misfunction. Secondly, to model the pattern of activities carried out by each user, a significant amount of data is needed. This is impractical especially in the actual era of Big Data with effortless access to public repositories. In order to deal with these problems, this paper investigates the use of Transfer Learning, specifically Unsupervised Domain Adaptation, within human activity recognition systems. The yielded experiment results reveal a useful transfer of knowledge and more importantly the convenience of transfer learning within human activity recognition. Apart from the delineated experiments, our work also contributes to the field of transfer learning in general through an exhaustive survey on transfer learning for human activity recognition based on wearables.

Keywords

Human activity recognition Transfer learning Unsupervised domain adaptation 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversity of PortoPortoPortugal
  2. 2.ICMCUniversity of São PauloSão PauloBrazil
  3. 3.LIAAD-INESC TECPortoPortugal
  4. 4.University of TwenteEnschedeNetherlands

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