The SEBA system: a novel approach for assessing psychological stress continuously at the workplace

Stress at work is a major cause of health problems for the employees and of costs for companies and the healthcare system. To prevent stress-related disorders, first both the stress level and the exposition to possible stressors must be known. The SEBA system assesses both and produces live data streams that are constantly and automatically evaluated. The system is a head-worn portable device. Multiple sensors assess biosignals of the users that are known to be sensitive towards the feeling of stress (e.g., pulse, eye blink rate, breathing rate, brain activity) and ambient conditions that could influence the feeling of stress (e.g., air quality, flickering lights, temperature, draft). SEBA classifies the individual stress level using a neural network and sends the processed data to a mobile application for visualization purposes. In this paper, we introduce the concept of the SEBA system, including its hardware, sensors, firmware, and software. The SEBA system is currently being development; the paper outlines the current state of development and possible obstacles.

[1]  Miikka Ermes,et al.  Automatic feature selection and classification of physical and mental load using data from wearable sensors , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[2]  Thomas Kubiak,et al.  Psychological and Psychophysiological Ambulatory Monitoring A Review of Hardware and Software Solutions , 2007 .

[3]  H. Selye A Syndrome produced by Diverse Nocuous Agents , 1936, Nature.

[4]  Francisco J. Pelayo,et al.  Portable System for Real-Time Detection of Stress Level , 2018, Sensors.

[5]  R. Adler,et al.  Engel's biopsychosocial model is still relevant today. , 2009, Journal of psychosomatic research.

[6]  R. Harris,et al.  Chronic and acute effects of stress on energy balance: are there appropriate animal models? , 2015, American journal of physiology. Regulatory, integrative and comparative physiology.

[7]  G. L. Engel The need for a new medical model: a challenge for biomedicine. , 2012, Psychodynamic psychiatry.

[8]  S. Folkman,et al.  Stress, appraisal, and coping , 1974 .

[9]  Ricardo Gutierrez-Osuna,et al.  Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors , 2012, IEEE Transactions on Information Technology in Biomedicine.

[10]  Juan Carlos Augusto,et al.  New Methods for Stress Assessment and Monitoring at the Workplace , 2019, IEEE Transactions on Affective Computing.

[11]  Emre Ertin,et al.  cStress: towards a gold standard for continuous stress assessment in the mobile environment , 2015, UbiComp.

[12]  Emre Ertin,et al.  Continuous inference of psychological stress from sensory measurements collected in the natural environment , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[13]  Gerhard Tröster,et al.  Monitoring Stress Arousal in the Wild , 2013, IEEE Pervasive Computing.

[14]  Stanley Richardson,et al.  Work Stress: Studies of the Context, Content and Outcomes of Stress – A Book of Readings , 2008 .

[15]  Robert Karasek,et al.  Healthy Work : Stress, Productivity, and the Reconstruction of Working Life , 1990 .

[16]  Walter Rohmert,et al.  Arbeitswissenschaftliche Beurteilung der Belastung und Beanspruchung an unterschiedlichen industriellen Arbeitsplätzen , 1975 .

[17]  M. Trumper Bodily Changes in Pain, Hunger, Fear and Rage: An Account of Recent Researches into the Function of Emotional Excitement , 1930, The Psychological Clinic.

[18]  Jin-Hyuk Hong,et al.  Affect Modeling with Field-based Physiological Responses , 2015, Interact. Comput..

[19]  J. D. McGaugh,et al.  Role of adrenal stress hormones in forming lasting memories in the brain , 2002, Current Opinion in Neurobiology.

[20]  N. Santanam,et al.  Cerebrovascular dysfunction with stress and depression , 2018, Brain circulation.