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Ubiquitous Monitoring of Boredom. H. Estépar 2 , D. Shastri 1 , A. Mandapati 1 , I. Pavlidis 1. (1) Department of Computer Science, University of Houston, Houston, TX 77204. dshastri , amandapa , [email protected],.

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Ubiquitous Monitoring of Boredom

H. Estépar2, D. Shastri1, A. Mandapati1, I. Pavlidis1

(1) Department of Computer Science, University of Houston,

Houston, TX 77204. dshastri, amandapa, [email protected],

(2) Department of Electrical and Computer Engineering, University of Puerto Rico, Mayagüez Campus

Mayagüez, Puerto Rico. [email protected]


Boredom in Context


  • To use Q-sensor to quantify the physiological state of boredom and compare it withrespect to GSR and facial thermal data
  • 1. Lowers heart rate and respiration rate (Similar to those experienced in sleepiness)
  • 2. Changes electro dermal activity (EDA)

Subjects under boredom tend to be more susceptive to distractions .

Classical example of boredom:

The work of security guards


  • Problem:
  • Need to stare at rarely interesting
  • video feeds
  • More susceptible to distractions
  • Any mistake can cost a lot

This highly accurate technology for measuring, temperature, EDA, and motion intensity (X,Y and Z co-ordinates) has never been used with such an objective.

The experimental results show that monitoring of the Q-sensor channels yields similar detecting power to GSR’s. and thermal imaging.

Wearable sensors

- Q-Sensor

Track boredom through physiological variables

Quantify physiological responses

Measurements Technologies



Combine symbiotic activities with passive monitoring

  • Mobility
  • - Wireless
  • Portable
  • Minimal obtrusive

Wired sensors


Contact-free sensors

-Thermal imagery

Example of security monitoring


EAGER Experiment

1. Experimental Design

1. Experiment Design

2. Data Analysis

Symbiotic Activities

Includes 2 wand games, 2 puzzle games, and 1 web browser.

  • The experiment lasts 4 hours (10 sessions)
  • The subject is asked to note suspicious
  • behavior they see.
  • 5 sessions of traditional monitoring,
  • and five with symbiotic activities.
  • The subject is monitored by thermal
  • imaging and contact sensors
  • The experiment lasts 4 minutes
  • The subject is asked to count circles that appear randomly on the monitor
  • The subject is monitored by thermal imaging and contact sensors
  • After every minute an auditory startle is delivered
  • Experimental Timeline
  • The experiment ends about 1 min after the delivery of the third startle

Step1: Extract thermalsignal from maxillaryregion, GSRand Q-sensorsignal

Step2: Down sample the collected data from signals

Step3:Normalize the signals

Step4: Reduce Q-sensor noise

Step5: Perform data analysis [signal correlation, Even Peak Time (ETP) and Even Peak Intensity (EPI)]


Thermal camera

Thermal GSR Q-sensor






Time [s]

Time [s]

Validation Results

EAGER Results


  • Q-sensor can be used effectively to measure temperature, EDA and motion intensity of the psychological state of boredom.
  • Subjects presented higher mean EDA and were more active when exposed to a symbiotic activity.
  • The obtained signal from Q-sensor is similar compared with those of GSR and thermal.

Even Peak Time (EPT)

Even Peak Intensity (EPI)


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.


Summary: Mean EDA with activityis greater than without activity

(exception - D006)