Parallel Statistical and Machine Learning Methods for Estimation of Physical Load

  • Sergii Stirenko
  • Peng Gang
  • Wei Zeng
  • Yuri GordienkoEmail author
  • Oleg Alienin
  • Oleksandr Rokovyi
  • Nikita Gordienko
  • Ivan Pavliuchenko
  • Anis Rojbi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11334)


Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue. They are based on the statistical analysis of accumulated and moving window data subsets with construction of a kurtosis-skewness diagram. This approach was applied to the data gathered by the wearable heart monitor for various types and levels of physical activities, and for people with various physical conditions. The different levels of physical activities, loads, and fitness can be distinguished from the kurtosis-skewness diagram, and their evolution can be monitored. Several metrics for estimation of the instant effect and accumulated effect (physical fatigue) of physical loads were proposed. The data and results presented allow to extend application of these methods for modeling and characterization of complex human activity patterns, for example, to estimate the actual and accumulated physical load and fatigue, model the potential dangerous development, and give cautions and advice in real time.


Statistical analysis Physiological signals Heart beat Classification Machine learning HCI and human behaviour 



The work was partially supported by Ukraine-France Collaboration Project (Programme PHC DNIPRO) ( and by Huizhou Science and Technology Bureau and Huizhou University (Huizhou, P.R.China) in the framework of Platform Construction for China-Ukraine Hi-Tech Park Project # 2014C050012001.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sergii Stirenko
    • 1
  • Peng Gang
    • 2
  • Wei Zeng
    • 2
  • Yuri Gordienko
    • 1
    Email author
  • Oleg Alienin
    • 1
  • Oleksandr Rokovyi
    • 1
  • Nikita Gordienko
    • 1
  • Ivan Pavliuchenko
    • 1
  • Anis Rojbi
    • 3
  1. 1.National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”KyivUkraine
  2. 2.Huizhou UniversityHuizhouChina
  3. 3.CHArt Laboratory (Human and Artificial Cognitions)University of Paris 8ParisFrance

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