Course title
M02260002
Introduction to Affective Computing

 SRIPIAN PEERAYA

LAOHAKANGVALVIT TIPPORN
Course description
This course provides a comprehensive introduction to Affective Computing, focusing on the theoretical foundations and practical applications of emotion recognition and analysis. Students will explore key topics such as measurement techniques, experimental design, the use of physiological sensors, and data analysis using statistical software.
Through a project-based learning approach, students will gain hands-on experience in collecting and interpreting affective data. The course emphasizes the development of essential research skills, including designing experiments, applying data visualization techniques, and performing statistical analyses using SPSS. Students will present their findings in both midterm and final presentations, demonstrating their ability to apply learned concepts to real-world scenarios.
Purpose of class
This course aims to introduce students to the fundamentals of Affective Computing, blending theory with practical skills in collecting, analyzing, and interpreting emotion-based data. Through hands-on projects, students will learn to design experiments, use tools like physiological sensors and SPSS software, and apply their knowledge to real-world problems. By the end, students will gain essential research, data analysis, and presentation skills applicable across various disciplines.
Goals and objectives

 Goals and objectives Course Outcomes
1. Students can explain the core concepts of Affective Computing and its applications in emotion-driven technologies.
A-1
2. Students can collect and analyze affective data using tools like physiological sensors and statistical software.
A-1
3. Students can apply affective computing knowledge to solve real-world problems through project-based learning.
A-1
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Homework Midterm project Final project Total.
1. 20% 5% 5% 30%
2. 5% 20% 20% 45%
3. 0% 10% 15% 25%
Total. 25% 35% 40% -
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Course orientation and overview, Introduction to Affective Computing and Affective Engineering Homework 100分
Review class material 90分
2. Methods for measuring affective data, Tools for data collection: subjective questionnaires and objective measurements Homework 100分
Review class material 90分
3. Experiment design (Part 1): Fundamentals of experimental design, Introduction to control variables and hypothesis testing Homework 100分
Review class material 90分
4. Experiment design (Part 2): Advanced topics in experimental design, Guest lecture on experiments involving human-robot interaction Homework 100分
Review class material 90分
5. Experiment design (Part 3): Practical considerations in experiment setup, Ethical issues in experiments involving human subjects Homework 100分
Review class material 90分
6. Experiment design (Part 4): Data collection techniques, Guest lecture on real-world applications and case studies in affective computing Minutes paper 100分
Review class material 90分
7. Mid-term presentations Preparation for midterm-presentation 190分
8. Introduction to physiological sensors, Hands-on practice with sensors Homework 100分
Review class material 90分
9. Techniques for data visualization Homework 100分
Review class material 90分
10. Data analysis methodologies, Statistical analysis using SPSS software Homework 100分
Review class material 90分
11. Project-based learning: Initiation and development Work on group project 190分
12. Progress report presentations Work on group project 130分
Prepare progress report 60分
13. Project-based learning: Continued development and data analysis Work on group project 190分
14. Final project presentation Preparation for final presentation 180分
Total. - - 2650分
Goals and objectives (Other Courses)
A:Fundamental Mechanical Engineering B:Advanced Mechanical Engineering C:Environment and Materials Engineering D:Chemistry and Biotechnology E:Electrical Engineering and Robotics G:Advanced Electronic Engineering F:Information and Communications Engineering L:Computer Science and Engineering H:Urban Infrastructure and Environment
Language
English
Evaluation method and criteria
Homework will contribute 25% to your grade.
The midterm presentation will contribute 35% to your grade.
The final presentation will contribute 40% to your grade.
Those who achieve at least 60% of the total score will pass this course.
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
授業内と授業外でフィードバックを行います。
Textbooks and reference materials
Picard, R. W. (1997). Affective computing. MIT Press.
Field, A. (2017). Discovering statistics using IBM SPSS statistics (5th ed.). Sage.
Prerequisites
None
Office hours and How to contact professors for questions
  • Weekdays: From 10:00 - 16:30 by email or face-to-face discussion at 11F, Main building, Toyosu campus (please make appointment time by email for face-to-face discussion)
    Dr. Peeraya Sripian: peeraya@shibaura-it.ac.jp
    Dr. Tipporn Laohakangvalvit: tipporn@shibaura-it.ac.jp
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
  • Course that cultivates a basic problem-solving skills
  • Course that cultivates a basic interpersonal skills
Active-learning course
Most classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 3.GOOD HEALTH AND WELL-BEING
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Thu Mar 13 04:12:18 JST 2025