Stressed or just running? Differentiation of mental stress and physical activity by using machine learning

Stressed or just running? Differentiation of mental stress and physical activity by using machine learning

Recently, modern people have excessive stress in their daily lives. With the advances in physiological sensors and wearable technology, people’s physiological status can be tracked, and stress levels can be recognized for providing beneficial services. Smartwatches and smartbands constitute the majority of wearable devices. Although they have an excellent potential for physiological stress recognition, some crucial issues need to be addressed, such as the resemblance of physiological reaction to stress and physical activity, artifacts caused by movements and low data quality. This paper focused on examining and differentiating physiological responses to both stressors and physical activity. Physiological data are collected in the laboratory environment, which contain relaxed, stressful and physically active states and they are differentiated successfully by using machine learning.

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  • [1] Hong JH, Ramos J, Dey A. Understanding physiological responses to stressors during physical activity. Proceedings of the ACM conference on ubiquitous computing 2012; 270-279.
  • [2] Liao W, Zhang W, Zhu Z, Ji Q, Gray WD. Toward a decision-theoretic framework for affect recognition and user assistance. Elsevier International Journal of Human-Computer Studies 2006; 64 (9): 847-873. doi: 10.1016/j.ijhcs.2006.04.001
  • [3] Cohen S, Janicki-Deverts D, Miller GE. Psychological stress and disease. Journal of American Medical Association 2007; 298 (14): 1685-1687. doi: 10.1001/jama.298.14.1685
  • [4] Plarre K, Raij A, Hossain S, Monowar A, Amin A et al. 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; Chicago, IL, USA; 2011. pp. 97-108.
  • [5] Li M. Multimodal physical activity recognition by fusing temporal and cepstral information. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2010; 18 (4): 369-380. doi: 10.1109/TNSRE.2010.2053217
  • [6] Wu W, Dasgupta S,Ramirez EE, Peterson C, Norman GJ. Classification accuracies of physical activities using smartphone motion sensors. Journal of medical Internet research 2012; 14 (5): e130. doi: 10.2196/jmir.2208
  • [7] Degroote L, De Bourdeaudhuij I, Verloigne M, Poppe L, Crombez G. The accuracy of smart devices for measuring physical activity in daily life: validation study. JMIR mHealth and uHealth 2018; 6 (12): e10972. doi: 10.2196/10972
  • [8] Dobbins C, Rawassizadeh R. Towards Clustering of Mobile and Smartwatch Accelerometer Data for Physical Activity Recognition. Informatics 2018; 5 (2). doi: 10.3390/informatics5020029
  • [9] Davoudi A. Accuracy of samsung gear s smartwatch for activity recognition: Validation study. JMIR mHealth and uHealth 2019; 7 (2): e11270. doi: 10.2196/11270
  • [10] Hao T, Walter KN, Ball MJ, Chang HY, Sun S et al. StressHacker: towards practical stress monitoring in the wild with Smartwatches. In American Medical Informatics Association Annual Symposium Proceedings; Washington D.C., USA; 2017. pp. 830.
  • [11] de Arriba-Pérez F, Santos-Gago JM, Caeiro-Rodríguez M, Ramos-Merino M. Study of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearables. Journal of Ambient Intelligence and Humanized Computing 2019; 10 (12): 4925-4945.
  • [12] Siirtola P. Continuous Stress Detection Using the Sensors of Commercial Smartwatch. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers; London, UK; 2019. pp. 1198-1201.
  • [13] Can YS, Chalabianloo N, Ekiz D, Fernandez-Alvarez J, Riva G et al. Personal stress-level clustering and decisionlevel smoothing to enhance the performance of ambulatory stress detection with smartwatches. IEEE Access 2020; 38146-38163. doi: 10.1109/ACCESS.2020.2975351
  • [14] Kirschbaum C, Pirke KM, Hellhammer D. The ‘trier social stress test’–a tool for investigating 20 psychobiological stress responses in a laboratory setting. Neuropsychobiology 1993; 28 (1-2):76–81. doi: 10.1159/000119004
  • [15] Can YS, Chalabianloo N, Ekiz D, Fernandez-Alvarez J, Repetto C et al. Real-life stress level monitoring using smart bands in the light of contextual information. IEEE Sensors Journal 2020; 20 (15):8721-8730. doi: 10.1109/JSEN.2020.2984644
  • [16] Tarvainen MP et al. Kubios HRV - A Software for Advanced Heart Rate Variability Analysis. In 4th European Conference of the International Federation for Medical and Biological Engineering; Berlin, Germany; 2009. pp. 1022-1025.
  • [17] Can YS, Gokay D, Kılıç DR, Ekiz D, Chalabianloo N et al. How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life. Sensors 2020; 20 (3).
  • [18] Taylor S, Jaques N, Chen W, Fedor S, Sano A et al. Automatic identification of artifacts in electrodermal activity data. In 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); San Fransico, CA, USA; 2015. pp. 1934-1937.
  • [19] Gjoreski M, Luštrek M, Gams M, Gjoreski H. Monitoring stress with a wrist device using context. Journal of Biomedical Informatics 2017; 73 (1): 159-170.
  • [20] Tarvainen MP, Niskanen J, Lipponen JA, Ranta-Aho PO, Karjalainen PA. Kubios HRV–heart rate variability analysis software. Computer methods and programs in biomedicine 2014; 113 (1): 210-220.
  • [21] Alberdi A, Aztiria A, Basarab A. Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. Journal of biomedical informatics 2016; 59 (1): 49-75.
  • [22] Singh D, Vinod K, Saxena SC. Sampling frequency of the RR interval time series for spectral analysis of heart rate variability. Journal of Medical Engineering & Technology 2004; 28 (6): 263-272.
  • [23] Greco A, Marzi C, Lanata A, Scilingo EP, Vanello N. Combining Electrodermal Activity and Speech Analysis towards a more Accurate Emotion Recognition System. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019; 229-232.
  • [24] Setz C, Arnrich B, Schumm J, La Marca R, Tröster G et al. Discriminating stress from cognitive load using a wearable EDA device. IEEE Transactions on Information Technology in Biomedicine 2010; 14 (1): 410-417.
  • [25] Cinaz B, Arnrich B, Marca R, Tröster G. Monitoring of mental workload levels during an everyday life office-work scenario. Personal Ubiquitous Computing 2013; 17 (2): 229-239.
  • [26] Hill T, Lewicki P. Statistics: methods and applications: a comprehensive reference for science, industry, and data mining. Boston USA: Statsoft Incorporation, 2006.
  • [27] Alpaydın E. Introduction to machine learning. Cambridge, Massachusettes: The MIT Press, 2004.
  • [28] Edgar TW, Manz DO. Research methods for cyber security. Amsterdam, The Netherlands: Syngress, 2017.
  • [29] Can T. Gait Analysis and Fall Risk Assessment with Wearable Inertial Sensors. Ph.D., Bogazici University, Istanbul, TURKEY, 2019.