Mobile Stress Recognition and Relaxation Support with SmartCoping: User-Adaptive Interpretation of Physiological Stress Parameters

The paper describes a mobile solution for the early recognition and management of stress based on continuous monitoring of heart rate variability (HRV) and contextual data (activity, location, etc.). A central contribution is the automatic calibration of measured HRV values to perceived stress levels during an initial learning phase where the user provides feedback when prompted by the system. This is crucial as HRV varies greatly among people. A data mining component identifies recurrent stress situations so that people can develop appropriate stress avoidance and coping strategies. A biofeedback component based on breathing exercises helps users relax. The solution is being tested by healthy volunteers before conducting a clinical study with patients after alcohol detoxification.

[1]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[2]  K. Gould,et al.  Can lifestyle changes reverse coronary heart disease? The Lifestyle Heart Trial , 1990, The Lancet.

[3]  D. Ornish.  Can lifestyle changes reverse coronary heart disease? , 1993, World review of nutrition and dietetics.

[4]  M. van Eck,et al.  The Effects of Perceived Stress, Traits, Mood States, and Stressful Daily Events on Salivary Cortisol , 1996, Psychosomatic medicine.

[5]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[6]  J. Staessen,et al.  Power spectral analysis of heart rate variability by autoregressive modelling and fast Fourier transform: a comparative study. , 1998, Acta cardiologica.

[7]  D. Brodie,et al.  Effects of Short-Term Psychological Stress on the Time and Frequency Domains of Heart-Rate Variability , 2000, Perceptual and motor skills.

[8]  P. Bennett,et al.  An investigation into the relationship between salivary cortisol, stress, anxiety and depression , 2003, Biological Psychology.

[9]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.

[10]  Otto B. Walter,et al.  The Perceived Stress Questionnaire (PSQ) Reconsidered: Validation and Reference Values From Different Clinical and Healthy Adult Samples , 2005, Psychosomatic medicine.

[11]  Kazuyoshi Hirota,et al.  The Relationship Between Salivary Biomarkers and State-Trait Anxiety Inventory Score Under Mental Arithmetic Stress: A Pilot Study. , 2005, Anesthesia and analgesia.

[12]  Chao Li,et al.  Realization of stress detection using psychophysiological signals for improvement of human-computer interactions , 2005, Proceedings. IEEE SoutheastCon, 2005..

[13]  Dimitris N. Metaxas,et al.  Optical computer recognition of facial expressions associated with stress induced by performance demands. , 2005, Aviation, space, and environmental medicine.

[14]  R. Harris,et al.  Exaggerated response to mild stress in rats fed high-fat diet. , 2006, American journal of physiology. Regulatory, integrative and comparative physiology.

[15]  Igor Malinovsky,et al.  Preliminary Results of an Open Label Study of Heart Rate Variability Biofeedback for the Treatment of Major Depression , 2007, Applied psychophysiology and biofeedback.

[16]  S. Tanaka,et al.  Feasibility study on driver's stress detection from differential skin temperature measurement , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Jari Viik,et al.  Perceived Mental Stress and Reactions in Heart Rate Variability—A Pilot Study Among Employees of an Electronics Company , 2008, International journal of occupational safety and ergonomics : JOSE.

[18]  M. Siepmann,et al.  A Pilot Study on the Effects of Heart Rate Variability Biofeedback in Patients with Depression and in Healthy Subjects , 2008, Applied psychophysiology and biofeedback.

[19]  M. Greenberg,et al.  The Effects of Respiratory Sinus Arrhythmia Biofeedback on Heart Rate Variability and Posttraumatic Stress Disorder Symptoms: A Pilot Study , 2009, Applied psychophysiology and biofeedback.

[20]  Margaret E. Morris,et al.  Mobile Heart Health: Project Highlight , 2009, IEEE Pervasive Computing.

[21]  Martin L. Griss,et al.  Activity-Aware Mental Stress Detection Using Physiological Sensors , 2010, MobiCASE.

[22]  Amy J. Connolly,et al.  Coping with the Dynamic Process of Technostress, Appraisal and Adaptation , 2011, AMCIS.

[23]  Peter Rossmanith,et al.  Simulated Annealing , 2008, Taschenbuch der Algorithmen.

[24]  Burr Settles,et al.  From Theories to Queries: Active Learning in Practice , 2011 .

[25]  Yorgos Goletsis,et al.  Towards Driver's State Recognition on Real Driving Conditions , 2011 .

[26]  Ton Dijkstra,et al.  Cross-validation of bimodal health-related stress assessment , 2013, Personal and Ubiquitous Computing.

[27]  R. Gutierrez-Osuna,et al.  Removal of Respiratory Influences From Heart Rate Variability in Stress Monitoring , 2011, IEEE Sensors Journal.

[28]  Fakhri Karray,et al.  Survey on speech emotion recognition: Features, classification schemes, and databases , 2011, Pattern Recognit..

[29]  Tao Huang,et al.  Design and Implementation of a Wireless Healthcare System Based on Galvanic Skin Response , 2011, ICAIC.

[30]  Rosa María Baños,et al.  Personal Health Systems for Mental Health: The European Projects , 2011, MMVR.

[31]  Begoña García Zapirain,et al.  A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee , 2012, Sensors.

[32]  M. Swan.  Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen , 2012, Journal of personalized medicine.

[33]  René Riedl,et al.  On the biology of technostress: literature review and research agenda , 2012, DATB.

[34]  Kathryn A. Dowsland,et al.  Simulated Annealing , 1989, Encyclopedia of GIS.

[35]  Sazali Yaacob,et al.  DETECTION OF HUMAN STRESS USING SHORT-TERM ECG AND HRV SIGNALS , 2013 .

[36]  Akane Sano,et al.  Stress Recognition Using Wearable Sensors and Mobile Phones , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[37]  A. Muaremi,et al.  Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep , 2013, BioNanoScience.

[38]  P. Lehrer.  How Does Heart Rate Variability Biofeedback Work? Resonance, the Baroreflex, and Other Mechanisms , 2013 .

[39]  W. Zareba,et al.  Heart rate variability. , 2013, Handbook of clinical neurology.

[40]  Helge B. D. Sørensen,et al.  Classification of acute stress using linear and non-linear heart rate variability analysis derived from sternal ECG , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[41]  Murtaza Bulut,et al.  Mobile real-time arousal detection , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[42]  Jean-Philippe Thiran,et al.  Detecting emotional stress from facial expressions for driving safety , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[43]  Barbara L. O'Kane,et al.  Heart rate variability (HRV): an indicator of stress , 2014, Sensing Technologies + Applications.

[44]  René Riedl,et al.  Stress-Sensitive Adaptive Enterprise Systems: Theoretical Foundations and Design Blueprint , 2014 .

[45]  Mohamad Khalil,et al.  Driver stress level detection using HRV analysis , 2015, 2015 International Conference on Advances in Biomedical Engineering (ICABME).

[46]  S. Puttonen,et al.  Subjective stress, objective heart rate variability-based stress, and recovery on workdays among overweight and psychologically distressed individuals: a cross-sectional study , 2015, Journal of Occupational Medicine and Toxicology.

[47]  Andrea Gaggioli,et al.  Positive Technology for Helping People Cope with Stress , 2021, Research Anthology on Rehabilitation Practices and Therapy.

[48]  Naoyuki Kubota,et al.  Classifying Stress From Heart Rate Variability Using Salivary Biomarkers as Reference , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[49]  Ulrich Reimer,et al.  An Application Framework for Personalised and Adaptive Behavioural Change Support Systems , 2016, ICT4AgeingWell.