Computer Science 866 (Detailed Information)

Course Description

Affective Computing is computing that relates to, arises from, or deliberately influences emotion. In this course, we focus on computational methods for sensing user emotion, approaches for adapting computer systems based on emotional state, and human-computer interfaces for expressing emotion.

Instructor

Name: Dr. Regan Mandryk
Office: 373.1 Thorvaldson Bldg
Office Hours: by appointment
email: regan@cs.usask.ca

Textbook and Lecture Notes

There are no required textbooks for this course. All required readings for the course will be posted in PDF format on the course website or handed out in class. Partial lecture notes will be provided online, via the course website; however, lecture notes are not a substitute for attending class. The visual nature of the course content, combined with the interactive nature of the content presentation, and the heavy emphasis on discussion means that class attendance is essential to success in this course.

Course Website

The course website is hosted using moodle and accessible though the CS website: http://www.cs.usask.ca/classes/. Course announcements regarding assignments as well as other information will be communicated to the class via moodle. The student is responsible for reading this website regularly.

Lecture Topics

Please see the schedule for a list of topics and dates. The following topics may be covered but are subject to change:

  • Affective Computing Overview
    • Representing Emotions: Discrete and Dimensional Models
    • Emotion Elicitation Experiments
  • Sensing Emotion
    • Physiological Computing
    • Facial Expression Recognition
    • Interaction Histories (Keystrokes and Mouse)
    • Posture, Gait, and Movement
    • Linguistic Approaches
  • Adaptive Systems and Input
    • Brain-Computer Interfaces
    • Physiological Input
    • Adaptive Games
    • Adaptive Music
  • Expressing Emotions
  • Technical and Practical Skills
    • Experimental Design and Statistics
    • Collecting, Processing, and Analyzing Physiological Signals
    • Time Series Analysis
    • Classification using Machine Learning Toolkits

Computing Facilities

The project for this course could be completed using a variety of languages.

Visual Studio is available on the Windows machines in Spinks 311 and Spinks 360. Visual Studio is also available on the Windows Terminal Server, discworld.usask.ca.

Java and the Eclipse IDE are available on the Windows machines in Spinks 311 and Spinks 360. Java and accompanying IDEs are also available on the Windows Terminal Server, discworld.usask.ca.

Tcl/tk and the Wish console are available on all Windows, Mac OS, and Linux machines in the Department of Computer Science instructional labs or can be downloaded for free at http://www.activestate.com/downloads (select ActiveTcl under Language Downloads). Tcl/tk and the Wish Console are also available on the Windows Terminal Server, discworld.usask.ca and the Linux terminal server tuxworld.usask.ca.

Matlab is available on the Linux machines in the instructional lab in Spinks 320, under Linux on the Dual-boot Linux/Windows machines in Spinks 311 and approximately one-half of the machines in Spinks 315 and Spinks 360 labs are equipped with Linux and Matlab. Matlab is also available on the Windows Terminal Server, discworld.usask.ca and the Linux terminal server tuxworld.usask.ca.

SPSS is available on select machines in Spinks 311 and Spinks 360.

All other resources are available in the Human Computer Interaction Laboratory

Student Evaluation

Assignments 20%
Reading Critiques 25%
Discussion Seminars 10%
Project 40%
Class participation 5%

Assignments

A series of small assignments will be completed throughout the course to teach the practical skills need for the final project. Assignments will be available via the course website and will be graded by the course instructor. Whether assignments are completed in groups or individually will be specified in the assignment instructions.

Absolutely no late assignments will be accepted for credit. Absolutely no extensions will be provided for assignment due dates.

Reading Critiques

Readings will be used as tool to reinforce concepts learned in class and there will be weekly reading assignments over the course. All reading assignments must be completed individually, unless otherwise stated.

Absolutely no late reading assignments will be accepted for credit. Absolutely no extensions will be provided for reading assignment due dates.

Seminar Discussions

Students will be required to lead multiple seminar discussions based on the weekly readings. Quality of leadership of the discussion and preparedness for the discussion will contribute to the discussion component of the final grade (10%). This will be assigned by the course instructor.

Class Participation

Class time will be used for content presentation, examples, case studies, design exercises, and group interaction. The visual nature of the course content, combined with the interactive nature of the content presentation, means that class attendance is essential to success in this course. All in-class activities are improved when there is sufficient class participation. As such, 5% of the final grade will depend on class participation and will be assigned by the course instructor.

Project

This course requires completion of a project which has several marked deliverables throughout the term. The goal is to provide students with experience in research in Affective Computing.

There are multiple stages to the project, each with a milestone and deliverable. Please consult the course schedule (early and often) for timing of components and for details on the deliverables.

Project Grading Scheme

Each project component will be graded and given a weight of the total project grade (40% of grade).

  1. Proposal (10%)
  2. Implementation and Testing (30%)
  3. Project Report (40%)
  4. Project Presentation/Video (20%)
Submission of Project

Submission instructions for projects will be given in the descriptions of individual project components. Programming components will be submitted using moodle.

Late Project Policy

Absolutely no late project components will be accepted for grading. As the project components build upon each other, feedback will be provided on late projects, but the grade for the late component will be zero.

Project Extensions

Absolutely no extensions will be given for project components.

Academic Honesty

Students are expected to be academically honest in all of their scholarly work, including course assignments and examinations. Academic honesty is defined and described in the Department of Computer Science Statement on Academic Honesty and the University of Saskatchewan Website.

Please note that new policies and procedures governing Academic Misconduct have come into effect as of January 1, 2010.