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Energy Expenditure Calculation with Physical Activity Recognition Using Genetic Algorithms

  • Y. Anand
  • P. P. Joby
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Physical health is associated with physical activity, physical activity also ensures the wellbeing of the humans, physical activity is recognized using body worn sensors, and three Inertial Measurement units (IMU) are used to capture the data from the sensors. The activity recognition chain consists of Data Acquisition, Preprocessing, segmentation, Feature extraction, and Classification. Different levels of research are carried out on each stage. In feature selection genetic algorithms are used but the paper proposing the memetic algorithms an enhanced version of the genetic algorithms with local search in the each stage of genetic algorithm. This technique shall eliminate the chances of energy loss and consequently increase efficiency of the current system.

Keywords

Genetic algorithm Human activity recognition Preprocessing Feature extraction MET 

References

  1. 1.
    Janssen I, LeBlanc AG (2010) Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys 7(1):40CrossRefGoogle Scholar
  2. 2.
    Reiss A, Stricker D (2012) Introducing a new benchmarked dataset for activity monitoring. In: Proceeding of ISWC’12, pp 108–109Google Scholar
  3. 3.
    Jette M, Sidney K, Blumchen G (1990) Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity. Clin Cardiol 13(8):555–565CrossRefGoogle Scholar
  4. 4.
    Kozey SL, Lyden K et al (2010) Accelerometer output and MET values of common physical activities. US National Library of Medicine National Institutes of Health. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2924952/
  5. 5.
    Bulling A, Blanke U, Schiele B (2014) A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv 46(3):33:1–33:33CrossRefGoogle Scholar
  6. 6.
    Baldominos A, Isasi P, Saez Y (2017) Feature selection for physical activity recognition using genetic algorithms. IEE ExploreGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science and EngineeringMar Baselios Christian College of Engineering and TechnologyPeermadeIndia
  2. 2.Mar Baselios Christian College of Engineering and TechnologyPeermadeIndia

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