Stress Recognition with State Classification Considering Temporal Variation of Stress Responses

Junhyung Moon, Juneil Lee, Dongmi Cheon, Munhee Lee, Kyoungwoo Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To avoid and manage stress-related problems, several works have investigated how to recognize stress states of people by exploiting the machine learning. Most of works utilize the supervised learning which requires labels for data to be trained and tested. Thus, labels which represent stress state of each data such as stressed or not become definitely important. Conventional stress state classification methods assign labels to unit data per a certain experimental period by applying stressor appearances or self-evaluation scores. However, those methods ignore temporal variations in stress responses which are involuntarily triggered inside the body within a shorter period of time than an experimental period. Therefore, we propose a stress state classification method by considering not only user's subjective evaluations but also temporal changes of stress responses in short periods. For the demonstration, we label our experimental data of 40 subjects by using our proposed classification method and conventional ones, respectively. Then, we train and test stress recognition models with 6 machine learning algorithms and our implemented neural network ones based on the labeled data. Finally, binary stress recognition with our proposed classification method improves the recognition accuracy by up to 31.6% as compared to those with conventional techniques.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2852-2859
Number of pages8
ISBN (Electronic)9781728118673
DOIs
Publication statusPublished - 2019 Nov
Event2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States
Duration: 2019 Nov 182019 Nov 21

Publication series

NameProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
CountryUnited States
CitySan Diego
Period19/11/1819/11/21

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Biotechnology
  • Molecular Medicine
  • Modelling and Simulation
  • Health Informatics
  • Pharmacology (medical)
  • Public Health, Environmental and Occupational Health

Fingerprint Dive into the research topics of 'Stress Recognition with State Classification Considering Temporal Variation of Stress Responses'. Together they form a unique fingerprint.

  • Cite this

    Moon, J., Lee, J., Cheon, D., Lee, M., & Lee, K. (2019). Stress Recognition with State Classification Considering Temporal Variation of Stress Responses. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 2852-2859). [8983372] (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983372