Course title
M02260002
Introduction to Affective Computing

 SRIPIAN PEERAYA

RAJAGOPALAN UMAMAHESWARI

LAOHAKANGVALVIT TIPPORN
Middle-level Diploma Policy (mDP)
Program / Major mDP Goals
(改組前)先進国際課程 A-1 A-1 Students shall obtain basic and advanced knowledge and skills in mathematics, natural and computer sciences as well as presentation skills to communicate on their knowledge with scholars from various fields.
先進国際課程 A-1 A-1 Students shall obtain basic and advanced knowledge and skills in mathematics, natural and computer sciences as well as presentation skills to communicate on their knowledge with scholars from various fields.
先進国際課程 A-2 A-2 To suitably lead an international team in the future, students will be able to consider and make decisions on issues in various kinds of problems by grasping what kind of problems are tackled to solve in what way in a wide range of fields in science and technology.
(改組前)先進国際課程 A-2 A-2 To suitably lead an international team in the future, students will be able to consider and make decisions on issues in various kinds of problems by grasping what kind of problems are tackled to solve in what way in a wide range of fields in science and technology.
先進国際課程 B B　Ability to understand, respect, and accept diversity in a global society, and cooperate with people from various backgrounds for international teamwork.
(改組前)先進国際課程 B B　Ability to understand, respect, and accept diversity in a global society, and cooperate with people from various backgrounds for international teamwork.
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 data analysis 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.
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 statistical data analysis using data analysis 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. Students will present their findings in their final project presentation, demonstrating their ability to apply learned concepts to real-world scenarios.
Goals and objectives
  1. Students can explain the core concepts of Affective Computing and its applications in emotion-driven technologies.
  2. Students can collect and analyze affective data using tools like physiological sensors and statistical analysis software.
  3. Students can apply affective computing knowledge to solve real-world problems through project-based learning.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Quizzes, Homework Midterm examination Final project and presentation Total.
1. 5% 25% 10% 40%
2. 5% 0% 20% 25%
3. 5% 10% 20% 35%
Total. 15% 35% 50% -
Evaluation method and criteria
Quizzes, Homeworks 15%
Midterm examination 35%
Final project and presentation 50%
Those who achieve at least 60% of the total score will pass this course.
Language
English
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 100minutes
Review class material 90minutes
2. Methods for measuring affective data, Tools for data collection: subjective questionnaires and objective measurements Homework 100minutes
Review class material 90minutes
3. Experiment design (Part 1): Fundamentals of experimental design, Introduction to control variables and hypothesis testing Homework 100minutes
Review class material 90minutes
4. Experiment design (Part 2): Advanced topics in experimental design, Experiments involving human interaction Homework 100minutes
Review class material 90minutes
5. Experiment design (Part 3): Practical considerations in experiment setup, Ethical issues in experiments involving human subjects Homework 100minutes
Review class material 90minutes
6. Experiment design (Part 4): Data collection techniques, Real-world applications and case studies in affective computing Minutes paper 100minutes
Review class material 90minutes
7. Mid-term presentations Preparation for midterm-presentation 190minutes
8. Introduction to physiological sensors, Hands-on practice with sensors Homework 100minutes
Review class material 90minutes
9. Techniques for data visualization Homework 100minutes
Review class material 90minutes
10. Data analysis methodologies, Statistical analysis using data analysis software Homework 100minutes
Review class material 90minutes
11. Project-based learning: Initiation and development Work on group project 190minutes
12. Progress report presentations Work on group project 130minutes
Prepare progress report 60minutes
13. Project-based learning: Continued development and data analysis Work on group project 190minutes
14. Final project presentation Preparation for final presentation 180minutes
Total. - - 2650minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in the class
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 : Sat Mar 14 13:55:01 JST 2026