A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

Goal: This paper proposes a reliable stress and relaxation level estimation algorithm that is implemented in a portable, low-cost hardware device and executed in real time. The main objective of this work is to offer an affordable and “ready-to-go” solution for medical and personal environments, in which the detection of the arousal level of a person is crucial. Methods: To achieve meaningful identification of stress and relaxation, a fuzzy algorithm based on expert knowledge is built according to parameters extracted from physiological records. In addition to the heart rate, parameters extracted from the galvanic skin response and breath are employed to extend the results. Moreover, this algorithm achieves accurate results with a restricted computational load and can be implemented in a miniaturized low-cost prototype. The developed solution includes standard and actively shielded electrodes that are connected to an Arduino device for acquisition, while parameter extraction and fuzzy processing are conducted with a more powerful Raspberry Pi board. The proposed solution is validated using real physiological registers from 42 subjects collected using BIOPAC MP36 hardware. Additionally, a real-time acquisition, processing and remote cloud storage service is integrated via IoT wireless technology. Results: Robust identification of stress and relaxation is achieved, with F1 scores of 91.15% and 96.61%, respectively. Moreover, processing is performed using a 20-second sliding window; thus, only a small frame of context is required. Significance: This work presents a reliable solution for identifying stress and relaxation levels in real time, which can lead to the production of low-cost commercial devices for use in medical and personal environments.

[1]  H. Cai,et al.  An Experiment to Non-Intrusively Collect Physiological Parameters towards Driver State Detection , 2007 .

[2]  Sylvia D. Kreibig,et al.  Autonomic nervous system activity in emotion: A review , 2010, Biological Psychology.

[3]  C. R. Snyder,et al.  Handbook of positive psychology , 2002 .

[4]  Bahram Tarvirdizadeh,et al.  Fabrication of a portable device for stress monitoring using wearable sensors and soft computing algorithms , 2019, Neural Computing and Applications.

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

[6]  J. Cacioppo,et al.  Handbook Of Psychophysiology , 2019 .

[7]  Emil Jovanov,et al.  Stress monitoring using a distributed wireless intelligent sensor system. , 2003, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[8]  Eloy Irigoyen,et al.  Online robust R-peaks detection in noisy electrocardiograms using a novel iterative smart processing algorithm , 2020, Appl. Math. Comput..

[9]  István Vassányi,et al.  Stress Detection Using Low Cost Heart Rate Sensors , 2016, Journal of healthcare engineering.

[10]  Sergio Alexander Salinas,et al.  e-Health prototype system for cardiac telemonitoring , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  J. Gross,et al.  Emotion elicitation using films , 1995 .

[12]  G. Fink Stress: Concepts, Cognition, Emotion, and Behavior : Handbook of Stress Series Volume 1 , 2016 .

[13]  M. Csíkszentmihályi,et al.  Positive psychology. An introduction. , 2000, The American psychologist.

[14]  M. A. Reyna,et al.  Development of an ambulatory ECG system based on Arduino and mobile telephony for wireless transmission , 2014, 2014 Pan American Health Care Exchanges (PAHCE).

[15]  Eloy Irigoyen,et al.  Acquisition and Fuzzy Processing of Physiological Signals to Obtain Human Stress Level Using Low Cost Portable Hardware , 2017, SOCO-CISIS-ICEUTE.

[16]  E. Sokhadze Effects of Music on the Recovery of Autonomic and Electrocortical Activity After Stress Induced by Aversive Visual Stimuli , 2007, Applied psychophysiology and biofeedback.

[17]  Gonzalo Bailador,et al.  A Stress-Detection System Based on Physiological Signals and Fuzzy Logic , 2011, IEEE Transactions on Industrial Electronics.

[18]  J. Thayer,et al.  Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting , 2017, Front. Psychol..

[19]  P. Hamilton,et al.  Open source ECG analysis , 2002, Computers in Cardiology.

[20]  Ivan Grech,et al.  Body area network for wireless patient monitoring , 2008, IET Commun..

[21]  Johannes Wagner,et al.  From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[22]  M.S. Sharawi,et al.  Design and implementation of a human stress detection system: A biomechanics approach , 2008, 2008 5th International Symposium on Mechatronics and Its Applications.

[23]  Eloy Irigoyen,et al.  Detection of Stress Level and Phases by Advanced Physiological Signal Processing Based on Fuzzy Logic , 2016, SOCO-CISIS-ICEUTE.

[24]  W. Cannon,et al.  STRESSES AND STRAINS OF HOMEOSTASIS , 1935 .

[25]  Chuan-Yu Chang,et al.  Application of support vector regression for phyciological emotion recognition , 2010, 2010 International Computer Symposium (ICS2010).

[26]  Zhan Zhao,et al.  Detecting work-related stress with a wearable device , 2017, Comput. Ind..

[27]  Ferry Astika Saputra,et al.  Implementation of blood glucose levels monitoring system based on Wireless Body Area Network , 2016, 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).

[28]  Matjaz Gams,et al.  Monitoring stress with a wrist device using context , 2017, J. Biomed. Informatics.

[29]  F. Shaffer,et al.  A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability , 2014, Front. Psychol..

[30]  Dana Kulic,et al.  Anxiety detection during human-robot interaction , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[31]  P. Grossman,et al.  Toward understanding respiratory sinus arrhythmia: Relations to cardiac vagal tone, evolution and biobehavioral functions , 2007, Biological Psychology.

[32]  J. Vagedes,et al.  How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. , 2013, International journal of cardiology.

[33]  Javier Muguerza,et al.  A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis , 2019, IEEE Access.

[34]  Tamás D. Gedeon,et al.  Modeling a stress signal , 2014, Appl. Soft Comput..

[35]  Eloy Irigoyen,et al.  An enhanced fuzzy algorithm based on advanced signal processing for identification of stress , 2018, Neurocomputing.

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

[37]  Carlos José Pereira de Lucena,et al.  Enabling a Smart and Distributed Communication Infrastructure in Healthcare , 2016 .

[38]  S. Fisher Stress, health and disease. , 1993, British journal of hospital medicine.