Multi-modal emotional processing for SCOUT: Beyond the HCI Psychometrics methods

Human-Computer Interaction (HCI) research groups have recently attracted to the issue of emotion or affect especially in the examination of interaction and design. With recent technological advances, human users are able to interact with computers in ways which are almost impossible. New modalities for computer interaction with human emotion such as skin conductivity, heart rate, brain signals and physiological signals are emerging. It shows that emotion plays an important role in human communication and interaction, therefore allow people to express emotion beyond the verbal domain. This issue motivates the investigation of two modals of emotional processing in the application of HCI and User Interface Design (UID) areas. The result of this study is directed to the development of an affective interaction design storyboard tool called SCOUT. The paper addresses significant roles of Multi-modal Emotional Processing methods for SCOUT, which includes different types of Psychometric usability methods and Physiological emotional processing methods. The application of Psychometrics and Multi-modal Emotional Processing are then, analyzed. The results of the analysis revealed that the use of both processing methods would enrich the evaluation of emotion in human-computer interaction study.

[1]  J. Lagopoulos Electrodermal activity , 2007, Acta Neuropsychiatrica.

[2]  Michael Minge,et al.  Measuring multiple components of emotions in interactive contexts , 2006, CHI Extended Abstracts.

[3]  J. Cacioppo,et al.  The psychophysiology of emotion. , 1993 .

[4]  Paul Kline,et al.  A Psychometrics Primer , 2000 .

[5]  Lesley Axelrod,et al.  The affective connection: how and when users communicate emotion , 2004, CHI EA '04.

[6]  Banu Onaral,et al.  Biomedical Signals: Origin and Dynamic Characteristics; Frequency-Domain Analysis , 1999 .

[7]  K. J. Vicente,et al.  Spectral Analysis of Sinus Arrhythmia: A Measure of Mental Effort , 1987, Human factors.

[8]  K. Scherer What are emotions? And how can they be measured? , 2005 .

[9]  Andrew Dillon,et al.  Beyond usability: process, outcome and affect in human-computer interactions , 2001 .

[10]  J. Anttonen Using the EMFi chair to measure the user’s emotion-related heart rate responses , 2005 .

[11]  Johannes Wagner,et al.  From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[12]  P. Kline Handbook of Psychological Testing , 2013 .

[13]  Kasper Hornbæk,et al.  Current practice in measuring usability: Challenges to usability studies and research , 2006, Int. J. Hum. Comput. Stud..

[14]  J. Annett Subjective rating scales: science or art? , 2002, Ergonomics.

[15]  Robert D. Ward,et al.  Physiological responses to different WEB page designs , 2003, Int. J. Hum. Comput. Stud..

[16]  Tom Carey,et al.  ACM SIGCHI Curricula for Human-Computer Interaction , 1992 .

[17]  Steve J. Westerman,et al.  Usability Testing Emotion-Oriented Computing Systems: Psychometric Assessment , 2006 .

[18]  S. Salim Ethics And Information Privacy In Affective Computing , 2008 .

[19]  Merriam-Webster Webster's New World Dictionary of the American Language , 1976 .

[20]  D. Watson,et al.  Development and validation of brief measures of positive and negative affect: the PANAS scales. , 1988, Journal of personality and social psychology.

[21]  Dylan M. Jones,et al.  Refining the measurement of mood: The UWIST Mood Adjective Checklist , 1990 .

[22]  Roddy Cowie,et al.  FEELTRACE: an instrument for recording perceived emotion in real time , 2000 .

[23]  Jennifer Allanson Electrophysiologically Interactive Computer Systems , 2002, Computer.

[24]  G Pfurtscheller,et al.  Hidden Markov Models Used for the Offline Classification of EEG Data - Hidden Markov-Modelle, verwendet zur Offline-Klassifikation von EEG-Daten , 1999, Biomedizinische Technik. Biomedical engineering.

[25]  John L. Sibert,et al.  Heart rate variability: indicator of user state as an aid to human-computer interaction , 1998, CHI.

[26]  M. Angela Sasse,et al.  Do Users Always Know What's Good For Them? Utilising Physiological Responses to Assess Media Quality , 2000, BCS HCI.