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R esearch E xperience for U ndergraduates

Ubiquitous Monitoring of Boredom. R esearch E xperience for U ndergraduates . Henry Estépar García Mentors: Anitha Mandapati Dr. Ioannis Pavlidis Dr. Dvijesh Shastri. Agenda. Motivation Application Validation Process automation Conclusion . Introduction. Motivation

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R esearch E xperience for U ndergraduates

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  1. Ubiquitous Monitoring of Boredom Research Experience for Undergraduates Henry EstéparGarcía Mentors: AnithaMandapati Dr. IoannisPavlidis Dr. DvijeshShastri

  2. Agenda • Motivation • Application • Validation • Process automation • Conclusion

  3. Introduction • Motivation • Validation • Application • Process automation • Conclusion

  4. Introduction • Motivation • Validation • Application • Process automation • Conclusion Boredom: A psychological state that changes physiology • Lower heart rate and respiration rate (Similar to those experienced in sleepiness) [Dahlen et al.] • Change Electrodermalactivity (EDA) Track boredom through physiological variables Wearable sensors - Q-Sensor Measurements Technologies Wired sensors - ECG, EEG, GSR Contact-free sensors -Thermal imagery

  5. Physiological Measurements • Motivation • Validation • Application • Process automation • Conclusion Wired sensors: Measure skin conductance (EDA), temperature • Motion sensitive • Obtrusive measurement Contact-free sensors Thermal camera: Measures facial physiology • + Non-obtrusive measurement • Motion restriction • Expensive (~ $150,000)

  6. Why Q-sensor? • Motivation • Application • Validation • Process automation • Conclusion • A wireless, wearable sensor that measures physicochemical properties such as excitement, stress, fear, engagement, boredom and calmness • Advantages: • Mobility • Wireless • Portable • Minimal obtrusive • Measures: • Temperature • Electro dermal activity (EDA) • Motion intensity • (X,Y and Z co-ordinates)

  7. Agenda • Motivation • Application • Validation • Process automation • Conclusion

  8. Experiment Design • Motivation • Validation • Application • Process automation • Conclusion • About every minute an auditory startle is delivered • Experimental Timeline • The experiment ends about 1 min after the delivery of the third startle The experiment lasts 4 minutes Thermal Camera Q-sensor GSR • Psychophysiology experiment - Design: • The subject is asked to count circles that appear randomly on the monitor • The subject is monitored by thermal imaging and contact sensors

  9. Results • Motivation • Validation Results • Application • Process automation • Conclusion Even Peak Intensity (EPI) Even Peak Time (EPT)

  10. Agenda • Motivation • Application • Validation • Process automation • Conclusion

  11. Application • Motivation • Validation • Application • Process automation • Conclusion • Proposed solution - Combine symbiotic activity with the passive monitoring • Improve enjoyment factor • Classical example of boredom - The work of security guards • Need to stare at the rarely interesting video feeds (enjoyment factor) • Any mistake can cost a lot (performance factor) • Q-sensor • Quantify physiological responses

  12. Motivation • Validation • Application • Process automation • Conclusion Q-sensor EDA Summary: Mean EDA with activityis greater than without activity (exception - D006)

  13. Agenda • Motivation • Application • Validation • Process Automation • Conclusion

  14. Process Automation vs Manual Analysis • Motivation • Validation • Application • Process automation • Conclusion

  15. Input  Output • Motivation • Validation • Application • Process automation • Conclusion • Statistical tool • Input/Output Input Output

  16. Motivation • Validation • Application • Process automation • Conclusion Statistical tool design • Statistical tool • Input/Output Full-scale plug in (OTACS) Prototype

  17. Agenda • Motivation • Application • Validation • Process Automation • Conclusion

  18. Conclusion Acknowledgement • Q-sensor can be used effectively to measure temperature, EDA and motion intensity of the psychological state of boredom. • The obtained signal from Q-sensor is similar compared with those of GSR and thermal. • Subjects presented higher mean EDA and were more active when exposed to a symbiotic activity. • The developed tool computes statistical measurements for breathing, Q-sensor and thermal. This research was sponsored by NSF grant number SCI-0453498. Additional thanks to the UH Department of Computer Science, College of Natural Sciences and Mathematics, Dean of Graduate and Professional Studies, VP for Research, and the Provost’s Office.

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