Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures

Nowadays, the dynamic development of technology allows for the design of systems based on various information sources and their integration into hybrid expert systems. One of the areas of research where such systems are especially helpful is emotion analysis. The sympathetic nervous system controls emotions, while its function is directly reflected by the electrodermal activity (EDA) signal. The presented study aimed to develop a tool and propose a physiological data set to complement the psychological data. The study group consisted of 41 students aged from 19 to 26 years. The presented research protocol was based on the acquisition of the electrodermal activity signal using the Empatica E4 device during three exercises performed in a prototype Disc4Spine system and using the psychological research methods. Different methods (hierarchical and non-hierarchical) of subsequent data clustering and optimisation in the context of emotions experienced were analysed. The best results were obtained for the k-means classifier during Exercise 3 (80.49%) and for the combination of the EDA signal with negative emotions (80.48%). A comparison of accuracy of the k-means classification with the independent division made by a psychologist revealed again the best results for negative emotions (78.05%).

[1]  J. Owens,et al.  The acceptability of healthcare: from satisfaction to trust. , 2016, Community dental health.

[2]  B. Fredrickson.  The role of positive emotions in positive psychology. The broaden-and-build theory of positive emotions. , 2001, The American psychologist.

[3]  N. Yperen,et al.  Do high job demands increase intrinsic motivation or fatigue or both? The role of job control and job social support , 2003 .

[4]  R. P. McDonald,et al.  Test Theory: A Unified Treatment , 1999 .

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

[6]  Bruce S. McEwen,et al.  Embodying Psychological Thriving: Physical Thriving in Response to Stress , 2010 .

[7]  R. Trevethan,et al.  Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice , 2017, Front. Public Health.

[8]  H. Edelsbrunner,et al.  Efficient algorithms for agglomerative hierarchical clustering methods , 1984 .

[9]  L. Chipchase,et al.  Knowledge, skills and professional behaviours required by occupational therapist and physiotherapist beginning practitioners in work-related practice: a systematic review. , 2013, Australian occupational therapy journal.

[10]  Justin Halberda,et al.  PsiMLE: A maximum-likelihood estimation approach to estimating psychophysical scaling and variability more reliably, efficiently, and flexibly , 2015, Behavior Research Methods.

[11]  P. Ekman,et al.  Autonomic nervous system activity distinguishes among emotions. , 1983, Science.

[12]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[13]  Thi-Bich-Hanh Dao,et al.  Constrained Minimum Sum of Squares Clustering by Constraint Programming , 2015, CP.

[14]  Martha E. Crosby,et al.  Assessing Cognitive Load with Physiological Sensors , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[15]  Armando J Pinho,et al.  Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification , 2020, Sensors.

[16]  Tomasz Szurmik,et al.  Application of Original System to Support Specialist Physiotherapy D4S in Correction of Postural Defects as Compared to Other Methods—A Review , 2020 .

[17]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[18]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[19]  B. Fredrickson,et al.  The Undoing Effect of Positive Emotions , 2000, Motivation and emotion.

[20]  Ingrid Tonhajzerova,et al.  Heart Rate Variability and Electrodermal Activity as Noninvasive Indices of Sympathovagal Balance in Response to Stress , 2013 .

[21]  B. Fredrickson,et al.  Positive affect and the complex dynamics of human flourishing. , 2005, The American psychologist.

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

[23]  M. Jensen,et al.  Changes in beliefs, catastrophizing, and coping are associated with improvement in multidisciplinary pain treatment. , 2001, Journal of consulting and clinical psychology.

[24]  Alexander J. Rothman,et al.  Emotional states and physical health. , 2000, The American psychologist.

[25]  Ole Møller Nielsen,et al.  Wavelets in scientific computing , 1998 .

[26]  Rosalind W. Picard,et al.  A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity , 2010, IEEE Transactions on Biomedical Engineering.

[27]  Yang Wang,et al.  Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks , 2012, OZCHI.

[28]  Jeff T. Larsen,et al.  Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations. , 1998, Journal of personality and social psychology.

[29]  Jing He,et al.  A Review on Automatic Facial Expression Recognition Systems Assisted by Multimodal Sensor Data , 2019, Sensors.

[30]  H. Prendinger,et al.  Emotion Recognition from Electromyography and Skin Conductance , 2005 .

[31]  Susan Folkman,et al.  Stress, Positive Emotion, and Coping , 2000 .

[32]  Yang Wang,et al.  Detecting Users’ Cognitive Load by Galvanic Skin Response with Affective Interference , 2017, ACM Trans. Interact. Intell. Syst..

[33]  David Pollreisz,et al.  Assessment of Physiological Signals During Happiness, Sadness, Pain or Anger , 2016, MobiHealth.

[34]  Gerhard Tröster,et al.  Discriminating Stress From Cognitive Load Using a Wearable EDA Device , 2010, IEEE Transactions on Information Technology in Biomedicine.

[35]  Masahiro Mori,et al.  Emotional sweating response in a patient with bilateral amygdala damage. , 2003, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[36]  Kyriaki Kalimeri,et al.  Exploring multimodal biosignal features for stress detection during indoor mobility , 2016, ICMI.

[37]  Gabriella Brancaccio,et al.  Anatomy and Physiology of Sweat Glands , 2016 .

[38]  Fang Chen,et al.  Galvanic skin response (GSR) as an index of cognitive load , 2007, CHI Extended Abstracts.

[39]  Paolo Napoletano,et al.  Biometric Recognition Using Multimodal Physiological Signals , 2019, IEEE Access.

[40]  Hugo F Posada-Quintero,et al.  Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review , 2020, Sensors.

[41]  Luca Citi,et al.  cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing , 2016, IEEE Transactions on Biomedical Engineering.

[42]  Paul E. Spector,et al.  Using the Job-Related Affective Well-Being Scale (JAWS) to investigate affective responses to work stressors. , 2000, Journal of occupational health psychology.

[43]  S. Lins,et al.  Job-related affective well-being scale (Jaws): evidences of factor validity and reliability , 2008 .

[44]  Stefano Negrini,et al.  Why do we treat adolescent idiopathic scoliosis? What we want to obtain and to avoid for our patients. SOSORT 2005 Consensus paper , 2006, Scoliosis.

[45]  Andrzej W. Mitas,et al.  Methods of Therapy of Scoliosis and Technical Functionalities of DISC4SPINE (D4S) Diagnostic and Therapeutic System , 2020 .

[46]  Mingyang Liu,et al.  Human Emotion Recognition Based on Galvanic Skin Response Signal Feature Selection and SVM , 2016, 2016 International Conference on Smart City and Systems Engineering (ICSCSE).

[47]  R. Lazarus.  Progress on a cognitive-motivational-relational theory of emotion. , 1991, The American psychologist.

[48]  Hany Ferdinando,et al.  Emotion Recognition using cvxEDA-Based Features , 2018 .

[49]  Mohammad H. Mahoor,et al.  A wavelet-based approach to emotion classification using EDA signals , 2018, Expert Syst. Appl..

[50]  S. Folkman,et al.  Positive psychological states and coping with severe stress. , 1997, Social science & medicine.

[51]  Ronald Glaser,et al.  Emotions, morbidity, and mortality: new perspectives from psychoneuroimmunology. , 2002, Annual review of psychology.

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

[53]  H. Storm.  Changes in skin conductance as a tool to monitor nociceptive stimulation and pain , 2008, Current opinion in anaesthesiology.

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

[55]  Ingrid Tonhajzerova,et al.  Spectral and Nonlinear Analysis of Electrodermal Activity in Adolescent Anorexia Nervosa , 2020, Applied Sciences.

[56]  Inbal Nahum-Shani,et al.  Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data , 2016, CHI.

[57]  Andrzej W. Mitas,et al.  Behavioral and Physiological Profile Analysis While Exercising—Case Study , 2020 .

[58]  Andrzej W. Mitas,et al.  Methods for Assessing the Subject’s Multidimensional Psychophysiological State in Terms of Proper Rehabilitation , 2020 .