Prof. Nadia Berthouze

Professor in Affective Computing and Interaction

Location: Room 2.10

University College London
66 - 72 Gower St (2nd floor) - London - WC1E 6BT, U.K

Telephone: +44 20 3108 7067 (x 7067)

Email: n.berthouze at ucl.ac.uk


Research Interests

The premise of my research is that affect, emotion, and subjective experience should be factored into the design of interactive technology. Indeed, for technology to be truly effective in our social network, it should be able to adapt to the affective needs of each user group or even each individual. The aim of my research is to create systems/software that can sense the affective state of their users and use that information to tailor the interaction process. Body movement appears to be a promising medium for this goal: it supports cognitive processes, regulates emotions, and mediates affective and social communication. I am currently pursuing three lines of research looking at body movement as a medium to induce, recognize and measure the quality of experience of humans and in particularly of humans interacting and engaging through/with technology. I am trying to identify the various factors that affect the recognition process, including cross-cultural differences and task context. Finally, I am looking onto the existence of dialects in affective body movement communication, including avatar-specific dialects. I was awarded a 2 years International Marie Curie Reintegration Grant started in 2006 to investigate these issues in the clinical context and in the gaming industry . More information on the project AffectME supported by this grant can be found here.


Research Interests

My funded work has all been broadly in the area of designing and evaluating technology that is aware of their user's affective experience and can support, regulate or amplify it.

Current projects:

The UCLIC Affective Posture database:

The UCLIC Database of Affective Postures and Body Movements We are creating a database of affective postures and affective body movement. If you are interested in using it for academic purpose, please contact us n.berthouze@ucl.ac.uk or alk@cise.ufl.edu

  • Acted emotions: angry, fearful, happy, sad. The data have been collected using a VICON motion capture system.

  • Non acted affective states in computer game setting: frustration, concentration, triumphant, defeated. The data have been collected using a Gypsy5 (Animazoo UK Ltd.) motion capture system.

  • Non acted affective states in clinical setting (not yet available for distribution).

Completed projects:

  • AffectME: Affective Multimodal Engagement
  • KDIME: Kansei-based Image Retrieval Environment

Research Publications

Retrieving data...

Affective Interaction 

The lectures will aims at : 1) giving a basic introduction to the theory of emotion from psychology and neuroscience viewpoints and to understand its importance in human decision and communication processes; 2) addressing the challenges in designing and evaluating systems that are capable of affectively interacting with humans. Methods to design, measure and influence the affective experience will be taught. Examples of current applications (e.g. in entertainment, education, health, therapy, rehabilitation, service robotics) will be used to identify problems and design solutions. Finally, the ethical implications (e.g., privacy) of affective interactive systems will be discussed.

Affective Computing and HRI

COMPGI17 (Also taught as: COMPM082)
MSc in Machine Learning, Computer Science Department
Fundamentals of calculus, probability, statistics or have taken Supervised Learning in term 1 or Artificial Intelligence and Neural Computing in the third year. (GI02 Unsupervised Learning is a plus).
The module targets students who have no previous knowledge in cognitive science and emotion theory and therefore the aim of Part 1 of the module is to give a basic introduction to the theory of emotion from psychology and neuroscience viewpoints and to understand its importance in human decision and communication processes. Part 2 will concentrate on the application of machine learning techniques to emotion recognition by looking at current applications in entertainment, education, and health. Part 3 will focus on the challenges in designing robots that are capable of socially interacting with humans. Examples of current applications in entertainment, education, health, therapy, rehabilitation, service robotics, rescue robots will be used to identify problems and discuss machine learning solutions for the topics taught in Parts 2 and 3.

Design Experience 2

The aims of the module are to get students to develop their HCI design skills and explicitly reflect on that development. The module provides opportunities for students to develop and demonstrate:
  • knowledge and skills from all course modules
  • knowledge and skills of user-centred design processes
  • the ability to conduct user research
  • the ability to make effective use of design tools and techniques
  • the ability to work as part of a team
  • the ability to prepare and present a poster

and, overall, to develop the ability to justify the use of user-centred design processes and critically evaluate their contribution to the overall product. The module aims to put together skills and knowledge acquired in the separate modules of the rest of the course. The module employs a problem-based learning approach, whereby students must draw on relevant theory and methods to develop a successful and effective design for a specific user interface or human/machine system. The two-week long practical mini-project is followed by a poster presentation and by the writing of a reflective report. For details about the mini-project see the section on Project Description.

Research Associates

Dr Hongying Meng: multimodal emotion recognition in patients with chronic pain

Dr Mohsen Shafizadehkenari: recognition of emotion and pain level from emg signal in patient with chronic pain

Dr Harsimrat Singh: recognizing the hedonic experience of touch from EEG signals


PhD/EngD Students

Shakil Afzal: Facilitating emotion regulation in collaborative learning

Siti Ibrahim: Identifying affective appraisal processes in information seeking activity

Charles Ray:  Improving the choice of business communications media through measurement of the impact of NVC components.

Bernardino Romera-Paredes: Emotion and pain level recognition from body movement in patients with chronic pain

Aneesha Singh: A virtual coach to motivate and assist rehabilitation session in chronic pain patients


MSc Students

Mirjami Alakoskela: analysis of body movement in attention to/away from pain condition

Kim Byers: emotional contagion in interactive art

Page last modified on 13 jan 16 17:01 by Louise M F Gaynor