A Study on Hyperparameter Configuration for Human Activity Recognition

  • Kemilly D. GarciaEmail author
  • Tiago Carvalho
  • João Mendes-Moreira
  • João M. P. Cardoso
  • André C. P. L. F. de Carvalho
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)


Human Activity Recognition is a machine learning task for the classification of human physical activities. Applications for that task have been extensively researched in recent literature, specially due to the benefits of improving quality of life. Since wearable technologies and smartphones have become more ubiquitous, a large amount of information about a person’s life has become available. However, since each person has a unique way of performing physical activities, a Human Activity Recognition system needs to be adapted to the characteristics of a person in order to maintain or improve accuracy. Additionally, when smartphones devices are used to collect data, it is necessary to manage its limited resources, so the system can efficiently work for long periods of time. In this paper, we present a semi-supervised ensemble algorithm and an extensive study of the influence of hyperparameter configuration in classification accuracy. We also investigate how the classification accuracy is affected by the person and the activities performed. Experimental results show that it is possible to maintain classification accuracy by adjusting hyperparameters, like window size and window overlap, depending on the person and activity performed. These results motivate the development of a system able to automatically adapt hyperparameter settings for the activity performed by each person.


Human Activity Recognition Ensemble of classifiers Semi-supervised learning Mobile computing 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kemilly D. Garcia
    • 1
    • 3
    Email author
  • Tiago Carvalho
    • 2
  • João Mendes-Moreira
    • 2
  • João M. P. Cardoso
    • 2
  • André C. P. L. F. de Carvalho
    • 3
  1. 1.EWI-DMBUniversity of TwenteEnschedeThe Netherlands
  2. 2.INESC TEC, Faculty of EngineeringUniversity of PortoPortoPortugal
  3. 3.ICMCUniversity of São PauloSão CarlosBrazil

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