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Korea Multimedia Society (한국멀티미디어학회)
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Stress Identification and Analysis using Observed Heart Beat Data from Smart HRM Sensor Device

  • Pramanta, SPL Aditya (School of Electrical Engineering, Institute of Technology Bandung) ;
  • Kim, Myonghee (Dept. of IT Convergence and Application Engineering, PuKyong Nat. Univ.) ;
  • Park, Man-Gon (Dept. of IT Convergence and Application Engineering, PuKyong Nat. Univ.)
  • Received : 2017.03.31
  • Accepted : 2017.07.03
  • Published : 2017.08.31
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Abstract

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

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References

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