skip to main content
10.1145/3341105.3373853acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

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

Published: 30 March 2020 Publication History

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.

References

[1]
H Abdi and LJ Williams. 2010. Tukey's honestly significant difference (HSD) test. Encyclopedia of Research Design. Thousand Oaks, CA: Sage (2010).
[2]
R Bari, RJ Adams, MM Rahman, MB Parsons, EH Buder, and S Kumar. 2018. rconverse: Moment by moment conversation detection using a mobile respiration sensor. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (2018).
[3]
WB Cannon. 1916. Bodily changes in pain, hunger, fear, and rage: An account of recent researches into the function of emotional excitement. D. Appleton.
[4]
PE Greenwood and MS Nikulin. 1996. A guide to chi-squared testing. John Wiley & Sons.
[5]
M Hashemi. 2011. Language stress and anxiety among the English language learners. Procedia-Social and Behavioral Sciences (2011).
[6]
JA Healey and RW Picard. 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems (2005).
[7]
K Hovsepian, M al'Absi, E Ertin, T Kamarck, M Nakajima, and S Kumar. 2015. cStress: towards a gold standard for continuous stress assessment in the mobile environment. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM.
[8]
V Job, CS Dweck, and GM Walton. 2010. Ego depletion---Is it all in your head? Implicit theories about willpower affect self-regulation. Psychological Science (2010).
[9]
CH Jordan, W Wang, L Donatoni, and BP Meier. 2014. Mindful eating: Trait and state mindfulness predict healthier eating behavior. Personality and Individual differences (2014).
[10]
CD Katsis, N Katertsidis, G Ganiatsas, and DI Fotiadis. 2008. Toward emotion recognition in car-racing drivers: A biosignal processing approach. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans (2008).
[11]
C Kirschbaum, KM Pirke, and DH Hellhammer. 1993. The 'Trier Social Stress Test'-a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology (1993).
[12]
SD Kreibig. 2010. Autonomic nervous system activity in emotion: A review. Biological Psychology (2010).
[13]
D Kukolja, S Popović, M Horvat, B Kovač, and K Ćosić. 2014. Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications. International Journal of Human-computer Studies (2014).
[14]
W Lu, MM Nystrom, PJ Parikh, DR Fooshee, JP Hubenschmidt, JD Bradley, and DA Low. 2006. A semi-automatic method for peak and valley detection in free-breathing respiratory waveforms. Medical Physics (2006).
[15]
K Manley. 2015. Comparative study of foreign language anxiety in Korean and Chinese students. (2015).
[16]
DH McFarland. 2001. Respiratory markers of conversational interaction. Journal of Speech, Language, and Hearing Research (2001).
[17]
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, P Prettenhofer, R Weiss, V Dubourg, et al. 2011. Scikit-learn: achine learning in Python. Journal of Machine Learning Research (2011).
[18]
K Plarre, A Raij, SM Hossain, AA Ali, M Nakajima, M Al'Absi, E Ertin, T Kamarck, S Kumar, M Scott, et al. 2011. Continuous inference of psychological stress from sensory measurements collected in the natural environment. In Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks. IEEE.
[19]
BH Prasetio, H Tamura, and K Tanno. 2018. Ensemble support vector machine and neural network method for speech stress recognition. In International Workshop on Big Data and Information Security. IEEE.
[20]
MM Rahman, AA Ali, K Plarre, M Al'Absi, E Ertin, and S Kumar. 2011. mconverse: Inferring conversation episodes from respiratory measurements collected in the field. In Proceedings of the 2nd Conference on Wireless Health. ACM.
[21]
P Rajasekaran, G Doddington, and J Picone. 1986. Recognition of speech under stress and in noise. In IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE.
[22]
P Schmidt, A Reiss, R Duerichen, C Marberger, and K Van Laerhoven. 2018. Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. In Proceedings of the International Conference on Multimodal Interaction. ACM.
[23]
P Schmidt, A Reiss, R Duerichen, and K Van Laerhoven. 2018. Wearable affect and stress recognition: A review. ArXiv Preprint ArXiv:1811.08854 (2018).
[24]
C Schubert, M Lambertz, RA Nelesen, W Bardwell, J-B Choi, and JE Dimsdale. 2009. Effects of stress on heart rate complexity---a comparison between short-term and chronic stress. Biological Psychology (2009).
[25]
M Soleymani, J Lichtenauer, T Pun, and M Pantic. 2011. A multimodal database for affect recognition and implicit tagging. IEEE Transactions on Affective Computing (2011).
[26]
HJM Steeneken and JHL Hansen. 1999. Speech under stress conditions: overview of the effect on speech production and on system performance. In IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. IEEE.
[27]
A Steptoe. 1991. Invited review: The links between stress and illness. Journal of Psychosomatic Research (1991).
[28]
C Tsigos and GP Chrousos. 2002. Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. Journal of Psychosomatic Research (2002).
[29]
FH Wilhelm, EM Handke, and WT Roth. 2003. Detection of speaking with a new respiratory inductive plethysmography system. Biomedical Sciences Instrumentation (2003).
[30]
L Woodrow. 2006. Anxiety and speaking English as a second language. RELC Journal (2006).

Cited By

View all
  • (2022)Emotional Stress Recognition Using Electroencephalogram Signals Based on a Three-Dimensional Convolutional Gated Self-Attention Deep Neural NetworkApplied Sciences10.3390/app12211116212:21(11162)Online publication date: 4-Nov-2022
  • (2020)Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial SensorsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34322104:4(1-23)Online publication date: 18-Dec-2020

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing
March 2020
2348 pages
ISBN:9781450368667
DOI:10.1145/3341105
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 March 2020

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. machine learning
  2. respiration signal
  3. stress recognition
  4. verbal situation
  5. wearable device

Qualifiers

  • Research-article

Funding Sources

  • Institute of Information Communications Technology Planning Evaluation (IITP) by Korea government (MSIT)

Conference

SAC '20
Sponsor:
SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing
March 30 - April 3, 2020
Brno, Czech Republic

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)8
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2022)Emotional Stress Recognition Using Electroencephalogram Signals Based on a Three-Dimensional Convolutional Gated Self-Attention Deep Neural NetworkApplied Sciences10.3390/app12211116212:21(11162)Online publication date: 4-Nov-2022
  • (2020)Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial SensorsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34322104:4(1-23)Online publication date: 18-Dec-2020

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media