Respiration signal based two layer stress recognition across non-verbal and verbal situations

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

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

Abstract

It is effective to recognize one's stress state before the stress incurs several health problems. Various works have recognized stress state (e.g., stressed or not) utilizing multiple physiological signals which change as one becomes stressed. They have exploited the experimental data collected from stress-inducing experiments with verbal periods such as the socio-evaluative stressor. Since verbal behavior affects various physiological signals, the physiological changes during their experiments could be introduced by either or both of being under stress and verbal state. However, those works have not properly differentiated the changes due to being stressed with the ones introduced by verbal behavior. Therefore, we propose the 2-layer stress recognition method which classifies the presence of verbal situations in the first layer and then recognizes stress state within each situation in the second layer. We utilize respiration signals which clearly change according to not only being stressed but also the presence of speaking. Based on our experimental data of 75 participants, we demonstrate that stress recognition accuracy improves as 7% higher than those of conventional methods on average under the same machine learning algorithm. Further, exploiting different machine learning algorithms for each layer in our method achieves up to 84% recognition accuracy.

Original languageEnglish
Title of host publication35th Annual ACM Symposium on Applied Computing, SAC 2020
PublisherAssociation for Computing Machinery
Pages638-645
Number of pages8
ISBN (Electronic)9781450368667
DOIs
Publication statusPublished - 2020 Mar 30
Event35th Annual ACM Symposium on Applied Computing, SAC 2020 - Brno, Czech Republic
Duration: 2020 Mar 302020 Apr 3

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference35th Annual ACM Symposium on Applied Computing, SAC 2020
CountryCzech Republic
CityBrno
Period20/3/3020/4/3

Bibliographical note

Funding Information:
This work was supported by Institute of Information Communications Technology Planning Evaluation (IITP) grant funded by the Korea government(MSIT) [2016-0-00562(R0124-16-0002), Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly]

All Science Journal Classification (ASJC) codes

  • Software

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