TY - BOOK

T1 - Evaluation of facial expression feedback within self-report tools and an exploration of depression's symptomatology as facial cues

AU - Valev, Hristo

PY - 2021

Y1 - 2021

N2 - Self-reports are the most accurate form of assessing mood. They, can beadministered frequently, and self-report tools are valuable for quantifying andmonitoring one’s mental state of well-being. Traditionally, self-reports are provided using numerical or graphical scales, however, those are known to be prone to systematic errors in their measurements. Alternatively, facial expressions are intrinsically connected to emotional experiences, are a tool for us to communicate our emotions. We are well-versed in enacting or recognizing facial expressions. Hence, those are suitable representations for mood. Tools relying on facial expressions can expand the space for mood self-report technologies.Depression is an affective disorder, particularly pervasive in contemporarysociety. Its severity is typically measured on individual symptoms using screenerquestionnaires. However, when administered frequently, the assessment quality of those questionnaires is known to degrade significantly. Hence, by identifying salient features indicative of depression’s symptomatology in the face, facial expressionbased tools can capitalise on the strengths of self-reports and be used for assessing or monitoring depression’s severity.Herein, this thesis explores the design and implementation of four prototypes for mood self-reports iteratively. Three empirical studies evaluate the use of the method within three experimental contexts, by using text and images to elicit emotions in-situ and for monitoring mood in the wild. Therein, the method was evaluated quantitatively – by contrasting self-reports to those provided with the well-known visual analogue scale, and qualitatively – by identifying aspects of importance for facial expression-based tools and exploring user’s preferences. Thereafter, an exploratory study was conducted identifying, and visualizing facial features indicative of symptoms of depression as a step towards creating disorder-specific self-report instruments. Finally, EmotionAlly, a prototype for contextualized assessment, tracking, and visualisation of mood using computer-generated facial expressions was developed, integrating findings from preceding quantitative and qualitative evaluations.

AB - Self-reports are the most accurate form of assessing mood. They, can beadministered frequently, and self-report tools are valuable for quantifying andmonitoring one’s mental state of well-being. Traditionally, self-reports are provided using numerical or graphical scales, however, those are known to be prone to systematic errors in their measurements. Alternatively, facial expressions are intrinsically connected to emotional experiences, are a tool for us to communicate our emotions. We are well-versed in enacting or recognizing facial expressions. Hence, those are suitable representations for mood. Tools relying on facial expressions can expand the space for mood self-report technologies.Depression is an affective disorder, particularly pervasive in contemporarysociety. Its severity is typically measured on individual symptoms using screenerquestionnaires. However, when administered frequently, the assessment quality of those questionnaires is known to degrade significantly. Hence, by identifying salient features indicative of depression’s symptomatology in the face, facial expressionbased tools can capitalise on the strengths of self-reports and be used for assessing or monitoring depression’s severity.Herein, this thesis explores the design and implementation of four prototypes for mood self-reports iteratively. Three empirical studies evaluate the use of the method within three experimental contexts, by using text and images to elicit emotions in-situ and for monitoring mood in the wild. Therein, the method was evaluated quantitatively – by contrasting self-reports to those provided with the well-known visual analogue scale, and qualitatively – by identifying aspects of importance for facial expression-based tools and exploring user’s preferences. Thereafter, an exploratory study was conducted identifying, and visualizing facial features indicative of symptoms of depression as a step towards creating disorder-specific self-report instruments. Finally, EmotionAlly, a prototype for contextualized assessment, tracking, and visualisation of mood using computer-generated facial expressions was developed, integrating findings from preceding quantitative and qualitative evaluations.

U2 - 10.17635/lancaster/thesis/1683

DO - 10.17635/lancaster/thesis/1683

M3 - Doctoral Thesis

PB - Lancaster University

ER -