skip to main content
research-article

A Multi-feature and Time-aware-based Stress Evaluation Mechanism for Mental Status Adjustment

Authors Info & Claims
Published:25 January 2022Publication History
Skip Abstract Section

Abstract

With the rapid economic development, the prominent social competition has led to increasing psychological pressure of people felt from each aspect of life. Driven by the Internet of Things and artificial intelligence, intelligent psychological pressure detection systems based on deep learning and wearable devices have acquired some good results in practical application. However, existing studies argue that the psychological stress state is influenced by the current environment. They put much attention on the momentary features but ignore the dynamic change process of mental status in the time dimension. Besides, the lack of research in the general laws of psychological stress makes it difficult to quantitatively evaluate the stress status, resulting in the inability to perceive the stress state of users effectively. Thus, this article proposes an evaluation mechanism of psychological stress for adjusting the mental status of users. Specifically, we design a multi-dimensional feature space and a time-aware feature encoder, which integrate various stress features and capture time characteristics of stress state change. Moreover, a novel mental state model is proposed, which uses the pressure features with time characteristics to evaluate the pressure stress level. This model also quantifies the internal relationship between pressure features. Last, we establish a practicable testbed to demonstrate how to evaluate and adjust mental state of users by the proposed evaluation mechanism of psychological stress.

REFERENCES

  1. [1] Rosch P. J.. 1999. Reminiscences of Hans Selye and the birth of “stress.” Int. J. Emerg. Mental Health 1 (1999), 5966.Google ScholarGoogle Scholar
  2. [2] Quevedo M. Gunnar and K.. 2007. The neurobiology of stress and development. Annual Review of Psychology 58, 1 (2007), 145–173. DOI: 10.1146/annurev.psych.58.110405.085605Google ScholarGoogle Scholar
  3. [3] Sutter M. Kassis, S. L. Schmidt, D. Schreyer, and M.. 2020. Psychological Pressure and the Right to Determine the Moves in Dynamic Tournaments—Evidence from a Natural Field Experiment. Center for Research in Economics, Management and the Arts (CREMA) Working Paper Series.Google ScholarGoogle Scholar
  4. [4] Leung W. Cai, M. Chen, and V.. 2014. Towards gaming as a service. IEEE Internet Comput. 18, 3 (May–June 2014), 1218.Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Ramos-Merino F. de Arriba-Pérez, J. M. Santos-Gago, M. Caeiro-Rodríguez, and M.. 2019. Study of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearables. J. Amb. Intell. Human. Comput. 10, 12 (Dec. 2019), 49254945. DOI: https://doi.org/10.1007/s12652-019-01188-3Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Picard J. A. Healey and R. W.. 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transport. Syst. 6, 2 (June 2005), 156166. DOI: https://doi.org/10.1109/TITS.2005.848368 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. [7] Muhammad M. S. Hossain, S. U. Amin, M. Alsulaiman, and G.. 2019. Applying deep learning for epilepsy seizure detection and brain mapping visualization. ACM Trans. Multimedia Comput. Commun. Appl. 15, 1s, (Feb. 2019). DOI: https://doi.org/10.1145/3241056 Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Oostenveld A. M. Brouwer, M. A. Hogervorst, J. B. van Erp, T. Heffelaar, P. H. Zimmerman, and R.. 2012. Estimating workload using EEG spectral power and ERPs in the n-back task. J. Neural Eng. 9, 4 (Aug. 2012). DOI: https://doi.org/10.1088/1741-2560/9/4/045008Google ScholarGoogle Scholar
  9. [9] Muhammad M. S. Hossain and G.. 2018. Emotion recognition using deep learning approach from audio-visual emotional big data. Inf. Fusion 49, 2019 (Sep. 2018), 6978. DOI: https://doi.org/10.1016/j.inffus.2018.09.008Google ScholarGoogle Scholar
  10. [10] Jeon E. K. Goh, O. Y. Kim, and H. J.. 2017. Depression is a mediator for the relationship between physical symptom and psychological well-being in obese people. Clin. Nutrit. Res. 6, 2 (Apr. 2017), 8998. DOI: https://doi.org/10.1088/1741-2560/9/4/045008Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Magnani B. Gisi, Andrew D. Althouse, Abigail S. Mathier, A. Pusateri, Bruce L. Rollman, A. La Rosa, and Jared W.. 2020. The unmeasured burden: Contribution of depression and psychological stress to patient-reported outcomes in atrial fibrillation. Clin. Nutrit. Res. 302 (Mar. 2020), 7580. DOI: https://doi.org/10.1016/j.ijcard.2019.12.004Google ScholarGoogle Scholar
  12. [12] Decker S. Mellor and R.. 2020. Multiple jobholders with families: A path from jobs held to psychological stress through work-family conflict and performance quality. Employ. Respons. Rights J. 32, 1 (Mar. 2020), 121. DOI: https://doi.org/10.1007/s10672-020-09343-1Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] McGonigal Kelly. The Willpower Instinct. Avery Publishing Group Inc.Google ScholarGoogle Scholar
  14. [14] Schueller D. C. Mohr, M. Zhang, and S. M.. 2017. Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Ann. Rev. Clin. Psychol. 13, 1 (Mar. 2017), 2347. DOI: https://doi.org/10.1146/annurev-clinpsy-032816-044949Google ScholarGoogle Scholar
  15. [15] Jha A. O. Akmandor and N. K.. 2017. Keep the stress away with SoDA: Stress detection and alleviation system. IEEE Trans. Multi-scale Comput. Syst. 3, 4 (Oct.–Dec. 2017), 269282. DOI: https://doi.org/10.1109/TMSCS.2017.2703613Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Ahmed K. Ahammed and M. U.. 2020. Quantification of mental stress using complexity analysis of EEG signals. Biomed. Eng.: Applic. Basis Commun. 32 (Apr. 2020). DOI: https://doi.org/10.4015/S1016237220500118Google ScholarGoogle Scholar
  17. [17] Leung Y. Hao, Y. Miao, M. Chen , H. Gharavi and V.. 20216G cognitive information theory: A mailbox perspective. Big Data and Cognitive Computing 5, 4 (2021) 56. https://doi.org/10.3390/bdcc5040056Google ScholarGoogle Scholar
  18. [18] Hossain M. S.. 2017. Cloud-supported cyber-physical localization framework for patients monitoring. IEEE Syst. J. 11, 1 (Mar. 2017), 118127. DOI: https://doi.org/10.1109/JSYST.2015.2470644Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Yuan M. Chen, Y. Hao, K. Lin, L. Hu, and Z.. 2018. Label-less learning for traffic control in an edge network. IEEE Netw. 32, 6 (2018), 814.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Yoon Y. Jung and Y. Ik. 2017. Multi-level assessment model for wellness service based on human mental stress level. Multimedia Tools Applic. 76, 9 (May 2017), 1130511317. DOI: https://doi.org/10.1007/s11042-016-3444-9 Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. [21] Zhang B. Hwang, J. You, T. Vaessen, I. Myin-Germeys, C. Park, and B.. 2018. Deep ECGNet: An optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals. Telemed. J E Health 24, 10 (Oct. 2018), 753772. DOI: https://doi.org/10.1089/tmj.2017.0250Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Alghamdi K. Masood and M. A.. 2019. Modeling mental stress using a deep learning framework. IEEE Access 7 (2019), 6844668454. DOI: https://doi.org/10.1109/ACCESS.2019.2917718Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Lotte C. Jeunet, C. Mühl, and F.. Design and validation of a mental and social stress induction protocol towards load-invaogy-based detection. In Proceedings of the International Conference on Physiological Computing Systems.Google ScholarGoogle Scholar
  24. [24] Hogervorst A. M. Brouwer and M. A.. 2014. A new paradigm to induce mental stress: The Sing-a-Song Stress Test (SSST). Front. Neurosci. 8 (July 2014). DOI: https://doi.org/10.3389/fnins.2014.00224Google ScholarGoogle Scholar
  25. [25] al N. A. Rashid, M. N. Taib, S. Lias, et. 2011. Learners learning style classification related to IQ and stress based on EEG. Procedia-Soc. Behav. Sci. 29 (2011), 10611070. DOI: https://doi.org/10.1016/j.sbspro.2011.11.339Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Gedeon N. Sharma and T.. 2014. Modeling a stress signal. Appl. Soft Comput. 14 (2014), 5361. DOI: https://doi.org/10.1016/j.asoc.2013.09.019 Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. [27] Kiguchi F. Al-shargie, T. B. Tang, N. Badruddin, and M.. 2018. Towards multilevel mental stress assessment using SVM with ECOC: An EEG approach. Med. Biol. Eng. Comput. 56, 1 (Jan. 2018), 125136. DOI: https://doi.org/10.1007/s11517-017-1733-8Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Gedeon N. Sharma and T.. 2014. Modeling a stress signal. Appl. Soft Comput. 14 (Jan. 2014), 5361. DOI: https://doi.org/10.1016/j.asoc.2013.09.019 Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. [29] Eriksen H. Ursin and H. R.. 2004. Cognitive activation theory of stress. Psychoneuroendocrinology 29, 5 (June 2004), 567592. DOI: https://doi.org/10.1016/S0306-4530(03)00091-XGoogle ScholarGoogle ScholarCross RefCross Ref
  30. [30] Garhammer M.. 2002. Pace of life and enjoyment of life. J. Happ. Stud. 3 (Sep. 2002), 217256. DOI: https://doi.org/10.1023/A:1020676100938Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Su L. Wang, C. Kang, Z. Yin and F.. 2019. Psychological endurance, anxiety, and coping style among journalists engaged in emergency events: Evidence from China. Iran J. Publ. Health 48, 1 (Jan. 2019), 95102.Google ScholarGoogle Scholar
  32. [32] Pozuelo C. Graham and J. Ruiz. 2017. Happiness, stress, and age: How the U curve varies across people and places. J. Popul. Econ. 30, 1 (Jan. 2017), 225264. DOI: https://doi.org/10.1007/s00148-016-0611-2Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Robinson D. Acemoglu and J. A.. 2002. The political economy of the Kuznets curve. Rev. Devel. Econ. 6 (Dec. 2002), 183203.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Braithwaite L. E. Chaby, M. J. Sheriff, A. M. Hirrlinger, and V. A.. 2015. Can we understand how develop psychological stress enhances performance under future threat with the Yerkes-Dodson law? Commun. Integr. Biol. 8, 3 (July 2015), 225264. DOI: https://doi.org/10.1080/19420889.2015.1029689Google ScholarGoogle Scholar
  35. [35] Jiang Q. J.. 2001. The trait coping style questionnaire. Zhongguo Xingwei Yixue Kexue 10 (Jan. 2001), 3637.Google ScholarGoogle Scholar
  36. [36] Berkoff G. Zimet, S. Powell, G. Farley, S. Werkman, and K.. 1991. Psychometric characteristics of the Multidimen scale of perceived social support. J. Clin. Psychol. 47, 6 (Nov. 1991), 756–61.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Spiegel E. Cardeña, C. Koopman, C. Classen, L. C. Waelde, and D.. 2000. Psychometric Properties of the Stanford Acute Stress Reaction Questionnaire (SASRQ): A valid and reliable measure of acute stress. J. Traum. Stress 13, 3 (Jan. 2000), 719734.Google ScholarGoogle Scholar
  38. [38] Zwart T. I. Oei and F. M.. 2019. The assessment of life events: Self-administered questionnaire versus interview, Journal of Affective Disorders. In Proceedings of the 16th International Conference on Ubiquitous Robots (UR). DOI: https://doi.org/10.1109/URAI.2019.8768550Google ScholarGoogle Scholar
  39. [39] Lu Z. Li, C. Xu, J. Zhu, and B.. 1986. Multi-factor and multi-object optimization for foundation brake device in railway freight car. J. Affect. Disord. 10, 3 (May 1986), 185190 DOI: https://doi.org/10.1016/0165-0327(86)90003-0Google ScholarGoogle Scholar

Index Terms

  1. A Multi-feature and Time-aware-based Stress Evaluation Mechanism for Mental Status Adjustment

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 1s
      February 2022
      352 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3505206
      Issue’s Table of Contents

      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].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 25 January 2022
      • Revised: 1 April 2021
      • Accepted: 1 April 2021
      • Received: 1 December 2020
      Published in tomm Volume 18, Issue 1s

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    View Full Text

    HTML Format

    View this article in HTML Format .

    View HTML Format
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!