Artificial Emotion Generation Based on Personality, Mood, and Emotion for Life-Like Facial Expressions of Robots

International audienceWe can't overemphasize the importance of robot's emotional expressions as robots step into human's daily lives. So, the believable and socially acceptable emotional expressions of robots are essential. For such human-like emotional expression, we have proposed an emotion generation model considering personality, mood and history of robot's emotion. The personality module is based on the Big Five Model (OCEAN Model, Five Factor Model); the mood module has one dimension such as good or bad, and the emotion module uses the six basic emotions as defined by Ekman. Unlike most of the previous studies, the proposed emotion generation model was integrated with the Linear Dynamic Affect Expression Model (LDAEM), which is an emotional expression model that can make facial expressions similar to those of humans. So, both the emotional state and expression of robots can be changed dynamically

Relationship of early-life trauma, war-related trauma, personality traits, and PTSD symptom severity: a retrospective study on female civilian victims of war

Background: Consequences of war-related traumatisation have mostly been investigated in military and predominant male populations, while research on female civilian victims of war has been neglected. Furthermore, research of post-war posttraumatic stress disorder (PTSD) in women has rarely included early-life trauma in their prediction models, so the contribution of trauma in childhood and early youth is still unexplored. Objective: To examine the relationship of early-life trauma, war-related trauma, personality traits, and symptoms of posttraumatic stress among female civilian victims of the recent war in Croatia. Method: The cross-sectional study included 394 participants, 293 war-traumatised adult women civilians, and 101 women without war-related trauma. Participants were recruited using the snowball sampling method. The applied instruments included the Clinician-Administrated PTSD Scale (CAPS), the NEO Personality Inventory-Revised (NEO-PI-R), the War Stressors Assessment Questionnaire (WSAQ), and the Early Trauma Inventory Self Report-Short Form (ETISR-SF). A hierarchical multiple regression analysis was performed to assess the prediction model of PTSD symptom severity measured by CAPS score for current PTSD. Results: The prevalence of current PTSD (CAPS cut-off score=65) in this cohort was 20.7%. The regression model that included age, early-life trauma, war-related trauma, neuroticism, and extraversion as statistically significant predictors explained 45.8% of variance in PTSD symptoms. Conclusions: Older age, exposure to early-life trauma, exposure to war-related traumatic events, high neuroticism, and low extraversion are independent factors associated with higher level of PTSD symptoms among women civilian victims of war

Development of the Contentment with Life Assessment Scale (CLAS): Using Daily Life Experiences to Verify Levels of Self-Reported Life Satisfaction

life satisfaction, life satisfaction measurement, subjective well-being, contentment, daily diary methodology,