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SER594 Lecture 06

SER594 Lecture 06

Human Computer Interaction
Static Models
(201902)

B546a9b97d993392e4b22b74b99b91fe?s=128

Javier Gonzalez
PRO

April 22, 2019
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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
  2. Quiz 02 • User Experience • Color • Messages •

    Data Integration
  3. Data 1

  4. Overview Data Multimodal Synchronization Fusion Cleaning Normalization Remove Outliers

  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.
  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
  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
  8. Static Models 2

  9. Overview Data Multimodal Cleaning Modeling Static Dynamic

  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
  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 .
  12. Eureqa | Enter Data • Import data: Final = 50%

    midterm + 25% HW + 25%Quiz
  13. Eureqa | Prepare Data • Clean: Normalize and remove outliers

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

    Metric
  15. Eureqa | Results

  16. Eureqa | Review

  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
  18. Analyzing Data | Biometrics 3

  19. Reference | Tobii Eye Tracker

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

    GSR (kOmh) • Quality
  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?
  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.