Volume 15, Issue 3 (12-2018)                   JSDP 2018, 15(3): 59-74 | Back to browse issues page


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Golgouneh A, Tarvirdizadeh B. Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals. JSDP. 2018; 15 (3) :59-74
URL: http://jsdp.rcisp.ac.ir/article-1-611-en.html
Abstract:   (171 Views)

Stress has affected human’s lives in many areas, today. Stress can adversely affect human’s health to such a degree as to either cause death or indicate a major contributor to death. Therefore, in recent years, some researchers have focused to developing systems to detect stress and then presenting viable solutions to manage this issue.
Generally, stress can be identified through three different methods including (1) Psychological Evaluation, (2) Behavioral Responses and finally (3) Physiological Signals. Physiological signals are internal signs of functioning the body, and therefore nowadays are commonly used in various medical and non-medical applications. Since these signals are correlated with the stress, they have been commonly used in detection of the stress in humans. Photoplethysmography (PPG) and Galvanic Skin Response (GSR) are two of the most common signals which have been widely used in many stress related studies. PPG is a noninvasive method to measure the blood volume changes in blood vessels and GSR refers to changes in sweat gland activity that are reflective of the intensity of human emotional state.
Design and fabrication of a real-time handheld system in order to detect and display the stress level is the main aim of this paper. The fabricated stress monitoring device is completely compatible with both wired and wireless sensor devices. The GSR and PPG signals are used in the developed system. The mentioned signals are acquired using appropriate sensors and are displayed to the user after initial signal processing operation. The main processor of the developed system is ARM-cortex A8 and its graphical user interface (GUI) is based on C++ programming language. Artificial Neural Networks such as MLP and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to modeling and estimation of the stress index. The results show that ANFIS model have a good accuracy with a coefficient of determination values of 0.9291 and average relative error of 0.007.
 

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Type of Study: Research | Subject: Paper
Received: 2017/11/20 | Accepted: 2018/07/25 | Published: 2018/12/19 | ePublished: 2018/12/19

References
1. [1] J. Cacioppo, L. G. Tassinary, and G. G. Berntson, The Handbook of Psychophysiology, vol. 44., Cambridge University Press, 2007. [DOI:10.1017/CBO9780511546396] [PMID]
2. [2] H. Kurniawan, A. V Maslov, and M. Pechenizkiy, "Stress detection from speech and Galvanic Skin Response signals," Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems. pp. 209–214, 2013. [DOI:10.1109/CBMS.2013.6627790]
3. [3] A. Kaklauskas et al., "Web-based biometric computer mouse advisory system to analyze a user's emotions and work productivity," Eng. Appl. Artif. Intell., vol. 24, no. 6, pp. 928–945, Sep. 2011. [DOI:10.1016/j.engappai.2011.04.006]
4. [4] "Physiology," 2015. [Online]. Available: http://www.oxforddictionaries.com/definition/english/physiology.
5. [5] E. Risk and O. Report, OSH in figures: stress at work — facts and figures. Luxembourg,: Euro-pean Agency for Safety and Health at Work, 2009.z
6. [6] M. Bickford, "Stress in the workplace: A general overview of the causes, the effects, and the solutions," Canadian Mental Health Association Newfoundland and Labrador Division, pp. 1–3, 2005.
7. [7] W. Liao, W. Zhang, Z. Zhu, and Q. Ji, "A real-time human stress monitoring system using dynamic bayesian network," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops. p. 70, 2005.
8. [8] D. Carneiro, J. C. Castillo, P. Novais, A. Fernández-Caballero, and J. Neves, "Multimodal behavioral analysis for non-invasive stress detection," Expert Systems with Applications, vol. 39, no. 18, pp. 13376–13389, 2012. [DOI:10.1016/j.eswa.2012.05.065]
9. [9] T. Hayashi, Y. Mizuno-Matsumoto, E. Okamoto, M. Kato, and T. Murata, "An fMRI study of brain processing related to stress states," World Automation Congress (WAC), pp. 1–6, 2012 [PMCID]
10. [10] H. Lu et al., "Stresssense: Detecting stress in unconstrained acoustic environments using smartphones," Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 351-360. ACM, 2012. [DOI:10.1145/2370216.2370270]
11. [11] A. Muaremi, B. Arnrich, and G. Tröster, "Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep," Bionanoscience, vol. 3, no. 2, pp. 172–183, 2013. [DOI:10.1007/s12668-013-0089-2] [PMID] [PMCID]
12. [12] C. Epp, M. Lippold, and R. L. Mandryk, "Identifying emotional states using keystroke dynamics," in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 715–724, 2011.
13. [13] D. F. Dinges et al., "Optical computer recognition of facial expressions associated with stress induced by performance demands.," Aviat. Space. Environ. Med., vol. 76, no. 6 Suppl, pp. B172-82, 2005. [PMID]
14. [14] A. Alberdi, A. Aztiria, and A. Basarab, "Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review," J. Biomed. Inform., vol. 59, pp. 49–75, 2016. [DOI:10.1016/j.jbi.2015.11.007] [PMID]
15. [15] P. Zimmermann, S. Guttormsen, B. Danuser, and P. Gomez, "Affective computing—A rationale for measuring mood with mouse and keyboard," Int. J. Occup. Saf. Ergon., vol. 9, no. 4, pp. 539–551, 2003. [DOI:10.1080/10803548.2003.11076589] [PMID]
16. [16] M. Garbarino, M. Lai, D. Bender, R. W. Picard, and S. Tognetti, "Empatica E3—A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition," in Wireless Mobile Communication and Healthcare (Mobihealth), 2014 EAI 4th International Conference on, pp. 39–42, 2014,. [DOI:10.4108/icst.mobihealth.2014.257418]
17. [17] A. Golgouneh, A. Bamshad, B. Tarvirdizadeh, and F. Tajdari, "Design of a new, light and portable mechanism for knee CPM machine with a user-friendly interface," In Artificial Intelligence and Robotics (IRANOPEN), pp. 103-108. IEEE, 2016 [DOI:10.1109/RIOS.2016.7529498]
18. [18] B. Cinaz, B. Arnrich, R. La Marca, and G. Tröster, "Monitoring of mental workload levels during an everyday life office-work scenario," Pers. ubiquitous Comput., vol. 17, no. 2, pp. 229–239, 2013. [DOI:10.1007/s00779-011-0466-1]
19. [19] R.A. Shirvan, M.A. Khalilzadeh, S.E. Tahami, and V. Saadatian, "Comparision of linear and nonlinear feaaturs of heart rate variability signal to quantification of stress level using genetic algorithem and neural network," Signal and Data Processing, vol. 5, no. 2, p.p. 29-40, 2009.
20. [20] C. Maaoui, A. Pruski, and F. Abdat, "Emotion recognition for human-machine communi-cation," in Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ Inter-national Conference on, pp. 1210–1215, 2008. [PMID]
21. [21] M. T. Quazi, S. C. Mukhopadhyay, N. K. Suryadevara, and Y. M. Huang, "Towards the smart sensors based human emotion recog-nition," in Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International, pp. 2365–2370, 2012.
22. [22] K. Palanisamy, M. Murugappan, and S. Yaacob, "Multiple physiological signal-based human stress identification using non-linear classifiers," Elektron. ir Elektrotechnika, vol. 19, no. 7, pp. 80–85, 2013.
23. [23] J. Wijsman, B. Grundlehner, J. Penders, and H. Hermens, "Trapezius muscle EMG as predictor of mental stress," in Wireless Health 2010, pp. 155–163, 2010. [DOI:10.1145/1921081.1921100]
24. [24] S.-H. Seo, J.-T. Lee, and M. Crisan, "Stress and EEG. Convergence and hybrid information technologies," InTech. Available from http// www. intechopen. com/ books/ Converg. Stress., 2010.
25. [25] Y. Shi, M. Nguyen, P. Blitz, and B. French, "Personalized stress detection from physiological measurements," Int. Symp. Qual. Life Technol., no. September 2015, pp. 28–29, 2010.
26. [26] C. Z. Wei, "Stress emotion recognition based on RSP and EMG signals," in Advanced Materials Research, 2013, vol. 709, pp. 827–831. [DOI:10.4028/www.scientific.net/AMR.709.827]
27. [27] J. Zhai and A. Barreto, "Stress detection in computer users based on digital signal processing of noninvasive physiological variables," in Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE, pp. 1355–1358, 2006. [DOI:10.1109/IEMBS.2006.259421]
28. [28] V. Jeyhani, S. Mahdiani, M. Peltokangas, and A. Vehkaoja, "Comparison of HRV parameters derived from photoplethysmography and electrocardiography signals," in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 5952–5955. [DOI:10.1109/EMBC.2015.7319747]
29. [29] J. Peuscher, "Galvanic skin response (GSR)," 2In: TMSi. 2012.
30. [30] K. Peternel, M. Pogačnik, R. Tavčar, and A. Kos, "A presence-based context-aware chronic stress recognition system," Sensors, vol. 12, no. 11, pp. 15888–15906, 2012. [DOI:10.3390/s121115888] [PMID] [PMCID]
31. [31] K. Asai, "The Role of Head-Up Display in Computer-Assisted Instruction," arXiv Prepr. arXiv1001.0420, 2010.
32. [32] J. A. Healey and R. W. Picard, "Detecting stress during real-world driving tasks using physiological sensors," Intell. Transp. Syst. IEEE Trans., vol. 6, no. 2, pp. 156–166, 2005. [DOI:10.1109/TITS.2005.848368]
33. [33] J. Hernandez, P. Paredes, A. Roseway, and M. Czerwinski, "Under pressure: sensing stress of computer users," in Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 51–60, 2014.
34. [34] F.-T. Sun, C. Kuo, H.-T. Cheng, S. Buthpitiya, P. Collins, and M. Griss, "Activity-aware mental stress detection using physiological sensors," in International Conference on Mobile Computing, Applications, and Services, 2010, pp. 211–230.
35. [35] C. Setz, B. Arnrich, J. Schumm, R. La Marca, G. Tröster, and U. Ehlert, "Discriminating stress from cognitive load using a wearable EDA device," IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 2, pp. 410–417, 2010. [DOI:10.1109/TITB.2009.2036164] [PMID]
36. [36] A. de Santos Sierra, C. S. Ávila, J. G. Casanova, and G. B. Del Pozo, "A stress-detection system based on physiological signals and fuzzy logic," Ind. Electron. IEEE Trans., vol. 58, no. 10, pp. 4857–4865, 2011. [DOI:10.1109/TIE.2010.2103538]
37. [37]. R.A. Shirvan, M.A. Khalilzadeh, "Process and analysis of the photoplethysmography and heart rate variability signals to determine stress level," 16th Iranian Conference on Electrical Engineering, 2008.
38. [38]. A. Derakhshan, M.A. Khalilzadeh, M. Azarnoosh, A. Mohammadian, "Evaluate the changes in stress level using facial thermal imaging," 16th Iranian conference on Biomedical Engineering, 2008.
39. [39] K. Frank, P. Robertson, M. Gross, and K. Wiesner, "Sensor-based identification of human stress levels," in Pervasive Computing and Communications Workshops (PERCOM Wor-kshops), 2013 IEEE International Conference on, pp. 127–132, 2013. [DOI:10.1109/PerComW.2013.6529469]
40. [40] M. Morris and F. Guilak, "Mobile heart health: project highlight," IEEE Pervasive Comput., vol. 8, no. 2, pp. 57–61, 2009. [DOI:10.1109/MPRV.2009.31]
41. [41] V. Alexandratos, M. Bulut, and R. Jasinschi, "Mobile real-time arousal detection," in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4394–4398, 2014. [DOI:10.1109/ICASSP.2014.6854432]
42. [42] "Research Center for Development of Advanced Technologies." [Online]. Available: http://en.-rcdat.ir.
43. [43] J. A. Healey, "Wearable and automotive systems for affect recognition from physiology." Massa-chusetts Institute of Technology, 2000.
44. [44] M. E. Dawson, A. M. Schell, and D. L. Filion, "7 the electrodermal system," Handb. Psycho-physiol., vol. 159, 2007. [DOI:10.1017/CBO9780511546396.007]
45. [45] M. Bolanos, H. Nazeran, and E. Haltiwanger, "Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals," in Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE, pp. 4289–4294, 2006. [DOI:10.1109/IEMBS.2006.260607]
46. [46] W.-H. Lin, D. Wu, C. Li, H. Zhang, and Y.-T. Zhang, "Comparison of heart rate variability from PPG with that from ECG," in The International Conference on Health Informatics, pp. 213–215, 2014. [DOI:10.1007/978-3-319-03005-0_54]
47. [47] J. Rand, A. Hoover, S. Fishel, J. Moss, J. Pappas, and E. Muth, "Real-time correction of heart interbeat intervals," IEEE Trans. Biomed. Eng., vol. 54, no. 5, pp. 946–950, 2007. [DOI:10.1109/TBME.2007.893491] [PMID]
48. [48] G. G. Berntson and J. R. Stowell, "ECG artifacts and heart period variability: don't miss a beat!," Psychophysiology, vol. 35, no. 1, pp. 127–132, 1998. [DOI:10.1111/1469-8986.3510127] [PMID]
49. [49] P. D. Welch, "The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms," IEEE Trans. audio Elec-troacoust., vol. 15, no. 2, pp. 70–73, 1967. [DOI:10.1109/TAU.1967.1161901]
50. [50] A. Golgouneh, "Design and development of a portable system to continuous stress monitoring system using ARM processor," M.S. Thesis, University of Tehran, 2016.
51. [51] M. A. Hall, "Correlation-based feature selection of discrete and numeric class machine learning," 2000.
52. [52] T. M. Geronimo, C. E. D. Cruz, E. C. Bianchi, F. de Souza Campos, and P. R. Aguiar, "MLP and ANFIS Applied to the Prediction of Hole Diameters in the Drilling Process,". INTECH Open Access Publisher, 2013.
53. [53] P. Davis, "Levenberg-marquart methods and nonlinear estimation," Siam News, vol. 26, no. 6, pp. 1–12, 1993.
54. [54] L. A. Zadeh, "Fuzzy sets," Inf. Control, vol. 8, no. 3, pp. 338–353, 1965. [DOI:10.1016/S0019-9958(65)90241-X]
55. [55] J.-S. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE Trans. Syst. Man. Cybern., vol. 23, no. 3, pp. 665–685, 1993. [DOI:10.1109/21.256541]
56. [56] L.-X. Wang, "A Course in Fuzzy Systems and Control, Prentice-Hall PTR," Englewood Cliffs, NJ, 1997.
57. [57] M. Saidi, H. Hassanpoor, and A. Azizi Lari, "Proposed new sign al for real-time stress monitoring: Combination of physiological measures," AUT J. Electr. Eng., vol. 49, no. 1, pp. 11–18, 2017.

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