SER594 Lecture 06

Human Computer Interaction
Static Models
(201902)

Transcript

1. SER594
   Human-Computer Interaction
   Lecture 06
   Static Models
   Javier Gonzalez-Sanchez, PhD
   javiergs@asu.edu
   javiergs.engineering.asu.edu
   Office Hours: By appointment
   

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2. Quiz 02
   • User Experience
   • Color
   • Messages
   • Data Integration
   

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3. Data
   1
   

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4. Overview
   Data
   Multimodal
   Synchronization
   Fusion
   Cleaning
   Normalization
   Remove Outliers
   

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5. Summary
   Sensing
   Device
   (rate in Hz)
   Legacy Software
   Sensing
   (Input or Raw Data)
   Physiological responses and/or
   Emotion reported (output or sensed values)
   Emotiv© EEG
   headset
   (128 Hz)
   Emotiv© SDK Brain Waves
   EEG activity. Reported in 14 channels [16],: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6,
   F4, F8, and AF4.
   Face activity. Blink, wink (left and right), look (left and right), raise brow, furrow brow,
   smile, clench, smirk (left and right), and laugh.
   Emotions. Excitement, engagement, boredom, meditation and frustration.
   Standard Webcam
   (10 Hz)
   MIT Media Lab MindReader Facial Expressions Emotion. Agreeing, concentrating, disagreeing, interested, thinking and unsure.
   MIT skin conductance sensor
   (2 Hz)
   USB driver Skin Conductivity Arousal.
   MIT pressure sensor
   (6 Hz)
   USB driver Pressure One pressure value per sensor allocated into the input/control device.
   Tobii© Eye tracking
   (60 Hz)
   Tobii© SDK Eye Tracking Gaze point (x, y).
   MIT posture sensor
   (6 Hz)
   USB driver Pressure
   Pressure values in the back and the seat (in the right, middle and left zones) of a
   cushion chair.
   

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6. Integration | sparse
   [18] J. Liu, S. Ji, and J. Ye. SLEP: Sparse Learning with Efficient Projections. Arizona State University, 2009. http://www.public.asu.edu/~jye02/Software/SLEP.
   timestamp fixationIndex gazePointX gazePointY
   mappedFixationPo
   intX
   mappedFixationPo
   intY
   fixationDuration
   Short Term
   Excitement
   Long Term
   Excitement
   Engagement/Bored
   om
   Meditation Frustration Conductance agreement concentrating
   4135755652 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628
   4135755659 213 573 408 570 408 216
   4135755668 0.436697 0.521059 0.550011 0.335825 0.498908
   4135755676 213 566 412 570 408 216
   4135755692 213 565 404 570 408 216
   4135755709 213 567 404 570 408 216
   4135755714
   4135755726 213 568 411 570 408 216
   4135755742 213 568 409 570 408 216
   4135755759 213 563 411 570 408 216
   4135755761
   4135755776 213 574 413 570 408 216
   4135755792 213 554 402 570 408 216
   4135755809 214 603 409 696 405 216
   4135755824
   4135755826 214 701 407 696 405 216
   4135755842 214 697 403 696 405 216
   4135755859 214 693 401 696 405 216
   4135755876 214 700 402 696 405 216
   4135755892 214 701 411 696 405 216
   4135755909 214 686 398 696 405 216
   4135755918
   4135755926 214 694 399 696 405 216
   4135755942 214 694 407 696 405 216
   4135755959 214 698 404 696 405 216
   4135755964
   4135755976 214 704 398 696 405 216
   4135755992 214 693 415 696 405 216
   4135756009 214 696 411 696 405 216
   4135756025 215 728 406 804 387 183
   4135756027 0.436697 0.521059 0.550011 0.335825 0.498908 1 1
   

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7. Integration | state machine
   timestamp fixationIndex gazePointX gazePointY
   mappedFixationPo
   intX
   mappedFixationPo
   intY
   fixationDuration
   Short Term
   Excitement
   Long Term
   Excitement
   Engagement/Bored
   om
   Meditation Frustration Conductance agreement concentrating
   4135755652 213 574 414 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755659 213 573 408 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755668 213 573 408 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755676 213 566 412 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755692 213 565 404 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755709 213 567 404 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755714 213 567 404 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755726 213 568 411 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755742 213 568 409 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755759 213 563 411 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755761 213 563 411 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755776 213 574 413 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755792 213 554 402 570 408 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755809 214 603 409 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755824 214 603 409 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755826 214 701 407 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755842 214 697 403 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755859 214 693 401 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755876 214 700 402 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755892 214 701 411 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755909 214 686 398 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755918 214 686 398 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755926 214 694 399 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755942 214 694 407 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755959 214 698 404 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755964 214 698 404 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755976 214 704 398 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135755992 214 693 415 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135756009 214 696 411 696 405 216 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135756025 215 728 406 804 387 183 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   4135756027 215 728 406 804 387 183 0.436697 0.521059 0.550011 0.335825 0.498908 0.401690628 1 1
   

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8. Static Models
   2
   

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9. Overview
   Data
   Multimodal
   Cleaning
   Modeling
   Static
   Dynamic
   

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10. Classification | Regression
    • Function Approximation – define an equation
    (mathematical relationship)
    • Developing a model using historical data to make a
    prediction on new data where we do not have the
    answer.
    • Approximating a mapping function (f) from input
    variables (x) to output variables (y).
    • Assume that new data will behave in similar way
    

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11. Tool | Eureqa
    https://www.nutonian.com/download/eureqa-desktop-download/
    [19] Dubcˇa ́kova ́, R. Eureqa-so9ware review. Gene>c programming and evolvable machines. Genet. Program. Evol. Mach. (2010) online first. doi:10.1007/s10710- 010-9124-z .
    

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12. Eureqa | Enter Data
    • Import data:
    Final = 50% midterm + 25% HW + 25%Quiz
    

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13. Eureqa | Prepare Data
    • Clean: Normalize and remove outliers
    

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14. Eureqa | Define a search
    • Building Blocks and Error Metric
    

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15. Eureqa | Results
    

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16. Eureqa | Review
    

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17. Model
    • Final = 50% midterm + 25% HW + 25%Quiz
    • Final = 11.48
    + 0.50*MidTerm
    + 0.08*HW3
    + 0.05*Q1 + 0.04*Q2 + 0.05*Q3 + 0.05*Q4
    + 0.00*HW1*HW2
    Q5
    

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18. Analyzing Data | Biometrics
    3
    

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19. Reference | Tobii Eye Tracker
    

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20. Reference | ABM B-Alert Wireless EEG
    • EEG workload is correlated with increased working memory load
    and difficulty level in mental arithmetic and other complex problem
    solving tasks.
    • ABM has 2 workload models -- one model was built on a Forward
    digit span (FBDS) task (recommended to use, as it fits for ~85% of
    population) and
    • the other built on a backward digit span (BDS) task (fits ~15% of
    population).
    • ABM's data outputs also contain the mean probability between
    the FBDS and BDS model.
    https://www.memorylosstest.com/digit-span/
    

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21. Reference | Shimmer GSR
    • Battery
    • GSC (mVolts)
    • GSR (kOmh)
    • Quality
    

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22. Homework
    • What could be said about these 2 seconds in the
    life of our first subject?
    • What could be said about the 20 minutes in the life
    of our second subject?
    

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23. SER594 – Human Computer Interaction
    Javier Gonzalez-Sanchez
    javiergs@asu.edu
    Spring 2019
    Disclaimer. These slides can only be used as study material for the SER594 course at ASU.
    They cannot be distributed or used for another purpose.
    

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