Objective stress monitoring based on wearable sensors in everyday settings

References

• Selye H. The stress of life; 1956.
   Google Scholar
• Bakker J, Pechenizkiy M, Sidorova N. What’s your current stress level? detection of stress patterns from gsr sensor data. Proceedings of the Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on; IEEE; 2011. p. 573–580.
   Google Scholar
• 2016 compsych stresspulse survey; 2016.
   Google Scholar
• Sterling, P., Eyer, J. Allostasis: a new paradigm to explain arousal pathology. In: S. Fisher , J. Reason, editors. Handbook of life stress, cognition and health. New York (NY): John Wiley & Sons;1988. p. 629–649.
   Google Scholar
• Selye H. Stress in health and disease. Boston (MA): Butterworth-Heinemann; 2013.
   Google Scholar
• Glanz K, Schwartz MD. Stress, coping, and health behavior. Health behavior and health education: theory, research, and practice. Francisco (CA): Jossey-Bass. Vol. 4; 2008; p. 211–236.
   Google Scholar
• Miller LH, Smith AD, Rothstein L. The stress solution: an action plan to manage the stress in your life. New York (NY): Pocket; 1994.
   Google Scholar
• Schubert C, Lambertz M, Nelesen R, et al. Effects of stress on heart rate complexity–a comparison between short-term and chronic stress. Biol Psychol. 2009;80(3):325–332.
   PubMed Web of Science ®Google Scholar
• Andreou E, Alexopoulos EC, Lionis C, et al. Perceived stress scale: reliability and validity study in greece. Int J Environ Res Public Health. 2011;8(8):3287–3298.
   PubMed Web of Science ®Google Scholar
• Egleston BL, Miller SM, Meropol NJ. The impact of misclassification due to survey response fatigue on estimation and identifiability of treatment effects. Statist Med. 2011;30(30):3560–3572.
   PubMed Web of Science ®Google Scholar
• Davis A, Maney D, Maerz J. The use of leukocyte profiles to measure stress in vertebrates: a review for ecologists. Funct Ecol. 2008;22(5):760–772.
   Web of Science ®Google Scholar
• Greene S, Thapliyal H, Caban-Holt A. A survey of affective computing for stress detection: Evaluating technologies in stress detection for better health. IEEE Consumer Electron Mag. 2016;5(4):44–56.
   Web of Science ®Google Scholar
• Akmandor AO, Jha NK. Keep the stress away with soda: stress detection and alleviation system. IEEE Trans Multi-Scale Comp Syst. 2017;3(4):269–282.
   Web of Science ®Google Scholar
• Sun FT, Kuo C, Cheng HT, et al. Activity-aware mental stress detection using physiological sensors. Proceedings of the International Conference on Mobile Computing, Applications, and Services; Springer; 2010. p. 282–301.
   Google Scholar
• Dupre A, Vincent S, Iaizzo PA. Basic ECG theory, recordings, and interpretation. In: Handbook of cardiac anatomy, physiology, and devices. Totowa (NJ): Humana Press; 2005. p. 191–201.
   Google Scholar
• Klabunde R. Cardiovascular physiology concepts. Philadelphia (PA): Lippincott Williams & Wilkins; 2011.
   Google Scholar
• McCraty R, Shaffer F. Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Glob Adv Health Med. 2015;4(1):46–61.
   PubMedGoogle Scholar
• Kim HG, Cheon EJ, Bai DS, et al. Stress and heart rate variability: a meta-analysis and review of the literature. Psychiatry Investig. 2018;15(3):235–245.
   PubMed Web of Science ®Google Scholar
• Shaffer F, Ginsberg J. An overview of heart rate variability metrics and norms. Front Public Health. 2017;5:258.
   PubMed Web of Science ®Google Scholar
• Healey J, Picard RW. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transport Syst. 2005;6(2):156–166.
   Web of Science ®Google Scholar
• Acharya UR, Joseph KP, Kannathal N, et al. Heart rate variability. In: Advances in cardiac signal processing. Berlin, Heidelberg: Springer; 2007. p. 121–165.
   Google Scholar
• Stein PK, Reddy A. Non-linear heart rate variability and risk stratification in cardiovascular disease. Indian Pacing Electrophysiol J. 2005;5(3):210–220.
   PubMedGoogle Scholar
• Behbahani S, Dabanloo NJ, Nasrabadi AM. Ictal heart rate variability assessment with focus on secondary generalized and complex partial epileptic seizures. Adv Biores. 2013;4(1):50–58.
   Google Scholar
• Holmes TH, Rahe RH. The social readjustment rating scale. J Psychosom Res. 1967;11(2):213–218.
   PubMed Web of Science ®Google Scholar
• Stress assessment questionnaire; 2012. https://ptc.bps.org.uk/test-review/stress-assessment-questionnaire.
   Google Scholar
• Patten ML. Questionnaire research: a practical guide. Abingdon-on-Thames (UK): Routledge; 2016.
   Google Scholar
• Phellas CN, Bloch A, Seale C. Structured methods: interviews, questionnaires and observation. Res Soc Cult. 2011;3:181–205.
   Google Scholar
• Kirschbaum C, Pirke KM, Hellhammer DH. The “trier social stress test”–a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology. 1993;28(1–2):76–81.
   PubMed Web of Science ®Google Scholar
• Hellhammer DH, Wüst S, Kudielka BM. Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology. 2009;34(2):163–171.
   PubMed Web of Science ®Google Scholar
• Langley JN. The autonomic nervous system (pt. i). 1921. Brain. 1903;26(1):1–26.
   Google Scholar
• Wagner J, Kim J, André E. From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification. Proceedings of the 2005 IEEE international conference on multimedia and expo; IEEE; 2005. p. 940–943.
   Google Scholar
• Taelman J, Vandeput S, Spaepen A, et al. Influence of mental stress on heart rate and heart rate variability. Proceedings of the 4th European conference of the international federation for medical and biological engineering; Springer; 2009. p. 1366–1369.
   Google Scholar
• Das D, Bhattacharjee T, Datta S, et al. Classification and quantitative estimation of cognitive stress from in-game keystroke analysis using EEG and GSR. Proceedings of the Life Sciences Conference (LSC); 2017. IEEE; IEEE; 2017. p. 286–291.
   Google Scholar
• Ciabattoni L, Ferracuti F, Longhi S, et al. Real-time mental stress detection based on smartwatch. Proceedings of the 2017 IEEE International Conference on Consumer Electronics (ICCE); IEEE; 2017. p. 110–111.
   Google Scholar
• Fernández JRM, Anishchenko L. Mental stress detection using bioradar respiratory signals. Biomed Signal Process Control. 2018;43:244–249.
   Web of Science ®Google Scholar
• Liao CY, Chen RC, Tai SK. Emotion stress detection using eeg signal and deep learning technologies. Proceedings of the 2018 IEEE International Conference on Applied System Invention (ICASI); IEEE; 2018. p. 90–93.
   Google Scholar
• Xia L, Malik AS, Subhani AR. A physiological signal-based method for early mental-stress detection. Biomed Signal Process Control. 2018;46:18–32.
   Web of Science ®Google Scholar
• Koussaifi M, Habib C, Makhoul A. Real-time stress evaluation using wireless body sensor networks. Proceedings of the 2018 Wireless Days (WD); IEEE; 2018. p. 37–39.
   Google Scholar
• Memory game; 2007. https://www.mathworks.com/matlabcentral/fileexchange/14059-memory-a-k-a-concentration.
   Google Scholar
• Mosquito fly sound; 2014. https://www.youtube.com/watch?v=PYnVlOoxZWw.
   Google Scholar
• Lang PJ. International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical report. Gainesville (FL): University of Florida; 2005.
   Google Scholar
• Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18(6):643–662.
   Google Scholar
• Gupta A, Bhandari S. ECG noise reduction by different filters - a comparative analysis. Int J Res Comp Commun Technol. 2015;4(7):424–431.
   Google Scholar
• Deng Y, Wu Z, Chu CH, et al. Evaluating feature selection for stress identification. Proceedings of the 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI); IEEE; 2012. p. 584–591.
   Google Scholar
• Firouzi F, Rahmani AM, Mankodiya K, et al. Internet-of-things and big data for smarter healthcare: from device to architecture, applications and analytics; 2018.
   Google Scholar
• Mieronkoski R, Azimi I, Rahmani AM, et al. The internet of things for basic nursing care–a scoping review. Int J Nurs Stud. 2017;69:78–90.
   PubMed Web of Science ®Google Scholar
• Hall MA. Correlation-based feature selection for machine learning Hamilton (NZ): University of Waikato; 1999.
   Google Scholar
• Das D, Datta S, Bhattacharjee T, et al. Eliminating individual bias to improve stress detection from multimodal physiological data. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); IEEE; 2018. p. 5753–5758.
   Google Scholar
• Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. Am Statistician. 1992;46(3):175–185.
   Web of Science ®Google Scholar
• Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297.
   Web of Science ®Google Scholar
• Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):1–27:27. http://www.csie.ntu.edu.tw/cjlin/libsvm.
   Web of Science ®Google Scholar
• Hand DJ, Yu K. Idiot’s bayes–not so stupid after all? Int Statistical Rev. 2001;69(3):385–398.
   Web of Science ®Google Scholar
• Blum A, Mitchell T. Combining labeled and unlabeled data with co-training. Proceedings of the eleventh annual conference on Computational learning theory; ACM; 1998. p. 92–100.
   Google Scholar
• Hall M, Frank E, Holmes G, et al. The weka data mining software: an update. SIGKDD Explor Newsl. 2009;11(1):10–18.
   Google Scholar
• Kohavi R, et al. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the International Joint Conference on Articial Intelligenc, Montreal, Canada. Vol. 14; 1995. p. 1137–1145.
   Google Scholar
• Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci. 2004;44(1):1–12.
   PubMedGoogle Scholar
• James G. Majority vote classifiers: theory and applications [dissertation]. Stanford University; 1998.
   Google Scholar
• Pinheiro N, Couceiro R, Henriques J, et al. Can ppg be used for HRV analysis? Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); IEEE; 2016. p. 2945–2949.
   Google Scholar
• Pietilä J, Mehrang S, Tolonen J, et al. Evaluation of the accuracy and reliability for photoplethysmography based heart rate and beat-to-beat detection during daily activities. Proceedings of the 6th Embec & NBC 2017. Springer; 2017. p. 145–148.
   Google Scholar
• Naeinia EK, Azimib I, Rahmania AM, et al. A real-time ppg quality assessment approach for healthcare internet-of-things. Procedia Comput Sci. 2019;151(C).
   Google Scholar