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Exploring Eustress Versus Distress: Physiological Insights from User Studies

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This study investigates the physiological responses associated with eustress and distress during user interactions. By combining observational techniques with peripheral physiological measurements, we quantify emotional states and their impact on human performance. Key stress indicators such as heart rate, respiration rate, and perspiration are examined, revealing how emotions can influence optimal alertness and anxiety levels. Utilizing thermal imagery and machine learning, we classify facial expressions and provide insights into the qualitative and quantitative analyses of stress mechanisms.

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Exploring Eustress Versus Distress: Physiological Insights from User Studies

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  1. Eustressed or Distressed?Combining Physiology withObservation in User Studies • Avinash Wesley • Dr. Peggy Lindner (Co-Advisor) • Dr. IoannisPavlidis (Advisor)

  2. Stress Signs • Introduction • Methods • Results and Discussion • Acknowledgements • Stress Mechanism • Motivation • Background • Peripheral Physiological Measurement of Stress • Adrenergic response • Elevates heart rate, respiration rate, and blood pressure • Cholinergic response • Activates sweat glands on fingers and the perinasal area

  3. Physiology and Observation • Introduction • Methods • Results and Discussion • Acknowledgements • Stress Mechanism • Motivation • Background • Perspiratoryresponses are • Sympathetic in nature • Non-specific to positive or negative arousal Distress Eustress

  4. Emotions vs. Performance • Introduction • Methods • Results and Discussion • Acknowledgements • Stress Mechanism • Motivation • Background • An important goal in user studies: Study the role of emotions on human performance • Emotions can be quantified via physiological response • Physiological responses can be disambiguated via observation Performance HIGH Optimal Alertness Anxiety Disorganization Sleep LOW MEDIUM HIGH Arousal

  5. Perspiration Signal and Observation • Introduction • Methods • Results and Discussion • Acknowledgements • Stress Mechanism • Motivation • Background • Physiology • Perspiration extraction method usingThermal Imagery [1] • Observational Annotation of Emotions • Traditionally done using Visual Imagery • Manual Courtesy of Science channel [1] D. Shastri, A. Merla, P. Tsiamyrtzis, and I. Pavlidis. Imaging facial signs of neurophysiological responses. IEEETransactions on Biomedical Engineering, 56(2):477–484, 2009.

  6. Region Tracking • Introduction • Methods • Results and Discussion • Acknowledgements • Facial Expression Recognition • Field Study • Seven anatomical regions tracked over time by a dynamic template update tracker [2] [2] Y. Zhou, P. Tsiamyrtzis, and I. Pavlidis. Tissue tracking in thermo-physiological imagery through spatio-temporal smoothing. Proc. of the 12th Int. Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2009),5762:1092–1099, 2009.

  7. Pattern Classification • Introduction • Methods • Results and Discussion • Acknowledgements • Facial Expression Recognition • Field Study • Feature Vector • Classifier • Classify five action units (AU1+2, 4, 9, 10, and 12) • Multilayer Perceptron • 10-fold Cross Validation d(x,5): Euclidean distance between ROI-x and 5, (x 5) AU 1+2 Inner + Outer Eyebrow Raise

  8. Surgical Training • Introduction • Methods • Results and Discussion • Acknowledgements • Facial Expression Recognition • Field Study • Surgeon Pool (n=17) • Novices • Experienced • Tasks • Running string (Task-1) • Pattern cut (Task-2) • Intracorporeal suture (Task-3) • Dataset: 977 Thermal Clips

  9. Validation Results • Introduction • Methods • Results and Discussion • Acknowledgements • Quantitative Analysis • Qualitative Analysis • Conclusions • Using Thermal Imagery • 244 Facial Expressions • Ground Truth via Visual annotation • Method Accuracy 81.55% * Confusion matrix * Use of visual images instead of thermal images for display purpose only

  10. Results From The Field Study • Introduction • Methods • Results and Discussion • Acknowledgements • Quantitative Analysis • Qualitative Analysis • Conclusions • Distress is inversely related to experience • EN (Perinasal perspiratory signal on portions of negative emotions) Novice Experienced

  11. Example Visualizations • Introduction • Methods • Results and Discussion • Acknowledgements • Quantitative Analysis • Qualitative Analysis • Conclusions Eustress Distress

  12. Conclusions • Introduction • Methods • Results and Discussion • Acknowledgements • Quantitative Analysis • Qualitative Analysis • Conclusions • The proposed method is • Comprehensive (quantitative and qualitative) • Economical (single imaging modality with no labor) • Conducted a study design that is applicable to a broad class of Human Machine Interaction • Future Work • Expand the facial expression set • Apply the method to more field studies • Detection of pain onset-offset

  13. Introduction • Methods • Results and Discussion • Acknowledgements • Support provided by NSF award # IIS-0812526

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