Skip to main content Skip to navigation
Throughout the 2021-22 academic year, we will be prioritising face to face teaching as part of a blended learning approach that builds on the lessons learned over the course of the Coronavirus pandemic. Teaching will vary between online and on-campus delivery through the year, and you should read guidance from the academic department for details of how this will work for a particular module. You can find out more about the University’s overall response to Coronavirus at:

ES4E9-15 Affective Computing

School of Engineering
Undergraduate Level 4
Module leader
Tardi Tjahjadi
Credit value
Module duration
10 weeks
60% coursework, 40% exam
Study location
University of Warwick main campus, Coventry
Introductory description

Affective Computing is the inter-disciplinary study and development of systems that can recognise and interpret human affects (emotion). Information gathered from various sensors (e.g., video camera, speech detector and electroencephalogram (EEG)) are processed to recognise the appropriate affect responses.

Module web page

Module aims

This module aims to introduce: theoretical underpinnings (psychological, physiological and technological) of affect recognition; affect sensing involving signal processing, computer vision and machine learning; and the design and implementation of effective human-machine interface applications such as health monitoring, deception detection, gaming experience and learning technologies.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Theoretical underpinnings of affective computing from an interdisciplinary perspective encompassing the affective, cognitive, social, media, and brain sciences.
Affect recognition from facial expressions, body language, speech, physiology, contextual features, and multimodal combinations of these modalities.
Applications of affective computing in human-robot interactions, unobtrusive deception detection and health monitoring.

Learning outcomes

By the end of the module, students should be able to:

  • Integrate theories from multiple disciplines (Computer Science / Engineering / Psychology) in order to explain the main concepts of affective computing.
  • Evaluate and implement the principles of automated facial expression recognition.
  • Analyse and implement the principles of automated body language recognition
  • Examine the principles of physiology for affective computing.
  • Critique the applications of affective computing in human-robot interactions, unobtrusive deception detection and health monitoring.
Indicative reading list

Calvo RA, D’Mellor SK, Gratch J, Kappas A (Eds), The Oxford Handbook of Affective Computing, Oxford University Press, 2015, ISBN: 9780199942237.
Peter C, Beale R (Eds), Affect and Emotion in Human Computer Interaction: From Theory to Applications, Springer, 2008, ISBN: 9783540850984.
Picard R, Affective Computing, MIT Press, 2000, ISBN: 9780262661157.

Subject specific skills
  1. Ability to conceive, make and realise a component, product, system or process.

  2. Ability to be pragmatic, taking a systematic approach and the logical and practical
    steps necessary for, often complex, concepts to become reality.

  3. Ability to seek to achieve sustainable solutions to problems and have strategies for
    being creative and innovative.

Transferable skills
  1. Numeracy: apply mathematical and computational methods to communicate parameters, model and optimize solutions.

  2. Apply problem solving skills, information retrieval, and the effective use of general IT facilities.

  3. Ability to formulate and operate within appropriate codes of conduct, when faced with
    an ethical issue.

  4. Appreciation of the global dimensions of engineering, commerce and communication.

Study time

Type Required
Lectures 25 sessions of 1 hour (17%)
Seminars 5 sessions of 1 hour (3%)
Tutorials 3 sessions of 1 hour (2%)
Demonstrations 1 session of 3 hours (2%)
Private study 114 hours (76%)
Total 150 hours
Private study description

114 hours guided independent learning.


No further costs have been identified for this module.

You must pass all assessment components to pass the module.

Assessment group D4
Weighting Study time
Laboratory-based report and Seminar Quiz 30%
Assignment 30%


Online Examination 40%


~Platforms - QMP

  • Online examination: No Answerbook required
  • Students may use a calculator
  • Engineering Data Book 8th Edition
  • Graph paper
Feedback on assessment

Laboratory report: mark and comments; Seminar quiz: mark and comments; Written examination: mark. Cohort level feedback on examinations.

Past exam papers for ES4E9


This module is Optional for:

  • Year 4 of UESA-H117 MEng Engineering with Exchange Year
  • Year 1 of TESA-H800 Postgraduate Taught Biomedical Engineering

This module is Option list A for:

  • Year 4 of UESA-H163 MEng Biomedical Systems Engineering
  • UESA-H107 MEng Engineering
    • Year 4 of H107 Engineering MEng
    • Year 4 of H10C Engineering with Business Management MEng
    • Year 4 of H10G Engineering with Communications MEng
    • Year 4 of H10M Engineering with Fluid Dynamics MEng
    • Year 4 of H10K Engineering with Robotics MEng
    • Year 4 of H10D Engineering with Sustainability MEng
    • Year 4 of H10L Engineering with Systems Engineering MEng
  • Year 4 of UESA-H114 MEng Engineering
  • Year 5 of UESA-H109 MEng Engineering with Intercalated Year
  • Year 4 of UESA-HH31 MEng Systems Engineering

This module is Option list B for:

  • UESA-HH63 MEng Systems Engineering
    • Year 4 of HH63 Systems Engineering (MEng)
    • Year 4 of H63A Systems Engineering with Business Management
    • Year 4 of H63B Systems Engineering with Sustainability
  • Year 4 of UESA-HH3A MEng Systems Engineering with Exchange Year
  • Year 5 of UESA-HH64 MEng Systems Engineering with Intercalated Year
  • Year 4 of UCSA-G408 Undergraduate Computer Systems Engineering