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http://dx.doi.org/10.14346/JKOSOS.2020.35.1.62

Stress Assesment based on Bio-Signals using Random Forest Algorithm  

Lim, Taegyoon (Autonomous Mobile AGV Project Group, RIST)
Heo, Jeongheon (Engineering Solution Research Group, RIST)
Jeong, Kyuwon (School of Mechanical Engineering, Chungbuk National University)
Ghim, Heirhee (Department of Psychology, Chungbuk National University)
Publication Information
Journal of the Korean Society of Safety / v.35, no.1, 2020 , pp. 62-69 More about this Journal
Abstract
Most people suffer from stress during day life because modernized society is very complex and changes fast. Because stress can affect to many kind of physiological phenomena it is even considered as a disease. Therefore, it should be detected earlier, then must be released. When a person is being stressed several bio-signals such as heart rate, etc. are changed. So, those can be detected using medical electronics techniques. In this paper, stress assessment system is studied using random forest algorithm based on heart rate, RR interval and Galvanic skin response. The random forest model was trained and tested using the data set obtained from the bio-signals. It is found that the stress assessment procedure developed in this paper is very useful.
Keywords
stress; TSST-G; heart rate; RR-interval; galvanic skin response; random forest; generalized linear model;
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1 B. von Dawans, C. Kirschbaum and M. Heinrichs, "The Trier Social Stress Test for Groups (TSST-G): A New Research Tool for Controlled Simultaneous Social Stress Exposure in a Group Format", Psychoneuroendocrinology, Vol. 36, pp. 514-522, 2011.   DOI
2 S. W. Park, H. R. Ghim and H. J. Lee, "The Effect of Movies on Stress Relaxation", 36th Int. Conf. on Psychology and the Arts, Sigmund Freud University, Vienna, June 26-30, 2019.
3 M. Hauptmann, J. H. Lubin, P. Rosenberg, J. Wellmann and L. Kreienbrock, "The use of Sliding Time Windows for the Exploratory Analysis of Temporal Effects of Smoking Histories on Lung Cancer Risk", Statistics in Medicine, Vol. 19, pp. 2185-2194, 2000.   DOI
4 L. Breiman, "Random Forests", Machine Learning, Vol. 45, pp. 5-32, 2001.   DOI
5 A. Liaw and M. Wiener, "Classification and Regression by Random Forest", R News, Vol. 2/3, pp. 18-22, 2002.
6 Y. U. Jo and D. C. Oh, "Study on the EMG-based Grasp Gesture Classification using Deep Learning and Application to Active Prosthetics", J. of Institute of Control, Robotics and Systems (in Korean), Vol. 25, No. 3, pp. 229-234, 2019.   DOI
7 J. H. Choi, G. Y. Song and J. W. Lee, "Road Extraction Based on Random Forest and Color Correlogram", J. of Institute of Control, Robotics and Systems (in Korean), Vol. 17, No. 4, pp. 346-352, 2011.   DOI
8 S. M. Kim, T. H. Kim and D. H. Kim, "Autonomous Driving through Non-uniform Steering Angles Nodes Determination by Deep Learning", J. of Institute of Control, Robotics and Systems (in Korean), Vol. 25, No. 8, pp. 677-683, 2019.   DOI
9 A. Liaw and M. Wiener, Breiman and Cutler's Random Forests for Classification and Regression, 2018(https://cran.r-project.org/web/packages/randomForest/randomForest.pdf).
10 J. A. Russell, "A Circumplex Model of Affect", Journal of Personality and Social Psychology, Vol. 29, No. 6, pp. 1161-1178, 1980.   DOI
11 J. Bakker, M. Pechenizkiy and N. Sidorova, "What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data", 11th IEEE Int. Conf. on Data Mining Workshops, pp. 573-580, 2011.
12 AMIX, "Monitoring of Arduino-based PPG and GSR Signals through an Android Device", IEEE Engineering in Medicine and Biology Society, Int. Student Conf. Design Competition, pp. 1-13, 2016.
13 J. A. Healey and R. W. Picard, "Detecting Stress During Real-World Driving Tasks using Physiological Sensors", IEEE Trans. on Intelligent Transportation Systems, Vol. 6, No. 2, pp. 156-166, 2005.   DOI
14 S. Sriramprakash, V. D. Prasanna and O. V. R. Murthy, "Stress Detection in Working People", Procedia Computer Science, Vol. 115, pp. 359-366, 2017.   DOI
15 E. Garcia-Ceja, V. Osmani and O. Mayora, "Automatic Stress Detection in Working Environments From Smartphones' Accelerometer Data: A First Step", IEEE J. Biomed Health Inform, Vol. 20, No. 4, pp. 1053-1060, 2015.   DOI
16 V. J. Madhuri, M. R. Mohan and R. Kaavya, "Stress Management Using Artificial Intelligence", Int. Conf. on Advances in Computing and Communications, pp. 54-57, 2013.
17 M. A, Birkett, "The Trier Social Stress Test Protocol for Inducing Psychological Stress", J. Vis. Exp., No. 56, e3238, 2011.
18 S. R. Lee, K. Y. Park and C. Y. Lee, "Intelligent Driver Assistance Systems using Biosignal", Journal of ICROS, Vol. 13, No. 12, pp. 1186-1191, 2007.
19 C. Kirschbaum, K. M. Pirke and D. H. Hellhammer, "The 'Trier Social Stress Test'-A Tool for Investigating Psychobiological Stress Responses in a Laboratory Setting", Neuropsychobiology, Vol. 28, No.1-2, pp. 76-81, 1993.   DOI