COMP0053 Affective Computing and Human-Robot Interaction

This database contains the 2018-19 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Academic session

2018-19

Module

Affective Computing and Human-Robot Interaction

Code

COMP0053

Module delivery

1819/A7P/T2/COMP0053 Postgraduate

Related deliveries

1819/A7U/T2/COMP0053 Masters (MEng)

Prior deliveries

COMPGI17

Level

Postgraduate

FHEQ Level

L7

FHEQ credits

15

Term/s

Term 2

Module leader

Berthouze, Nadia

Contributors

Berthouze, Nadia

Module administrator

Abbaro, Besheer

Aims

The module targets students who have no previous knowledge in cognitive science and emotion theory and therefore the aim of Part A of the module is to give a basic introduction to the theory of emotion from psychology viewpoints and to understand its importance in human decision and communication processes. Part B will concentrate on the application of machine learning techniques to automatic emotion recognition by looking at current applications (e.g. in entertainment, education, and health) and available sensing technology. Part C will focus on the challenges in designing robots that are capable of socially interacting with humans. Examples of current applications (e.g. in entertainment, health, rehabilitation, service robotics) will be used to identify problems and discuss affective computing challenges and approaches for the topics taught in Parts A and B.

Learning outcomes

To have a basic knowledge of emotion models and of how technology (e.g., robot) can be endowed with the ability to affectively and socially interact with its user.To understand the challenges that affective computing and social HRI pose to the machine learning field and identify the advantages and disadvantages of different approaches to address those issues.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MRes Financial Computing
  • MRes Robotics
  • MRes Web Science and Big Data Analytics
  • MSc Computational Statistics and Machine Learning
  • MSc Data Science and Machine Learning
  • MSc Machine Learning
  • MSc Robotics and Computation
  • MSc Web Science and Big Data Analytics
  • MSc Data Science

Prerequisites:

In order to be eligible to select this module, students must have:

Content

Emotion theory

  • What is affect, emotion, mood?
  • Why do we have emotions?
  • Neurological and psychological perspectives
  • How do humans express and recognise emotions?
  • Emotion expression models, appraisal theories
  • Affective and social interaction

Affective computing

  • Affective computing
    • definition
    • aims and current challenges
    • applications

Emotion Recognition

  • How to select and use sensors for data collection
  • How to build an automatic emotion recognition system from:
    • single modality: facial expressions, body expressions, touch expressions, voice, biosignals
    • multimodal fusion

Human-Robot Interaction (HRI)

  • Social robotics: motivation and emotions in robots
  • Emotion based architecture
  • Ethical issues in Affective Computing and HRI research

An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.

Delivery

The module is delivered through a combination of lectures, tutorials, seminars, project work: building an automatic emotion recognition system.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Group project: building an emotion recognition system.

40

Some parts of the project will be carried out in group to facilitate group discussion and co-learning.

2

Individual and critical report of the project

60

 

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.

Resit and deferred assessments may be based on a new project to be carried out individually.