Quirós-Ramírez, M. Alejandra
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Design and Evaluation of a Loving Kindness Virtual Reality Meditation Experience
Loving-kindness meditation (LKM) is an ancient meditation technique based on generating kind intentions, compassion, and wishes of well-being towards oneself and others, while visualizing the target individual those intentions are directed to. Regular LKM practice results in more positive emotions, reduction of stress and increase in emotional processing, feelings of compassion and empathy. Here, we present our work in designing and evaluating a Virtual Reality (VR) experience aimed at supporting the LKM. Our results show that the LKM VR experience increases mindfulness, compassion, relaxation, as good as an audio meditation does. We discuss the advantages of the LKM VR experience, and future work.
Body Image Disturbances and Weight Bias After Obesity Surgery : Semantic and Visual Evaluation in a Controlled Study, Findings from the BodyTalk Project
Purpose:
Body image has a significant impact on the outcome of obesity surgery. This study aims to perform a semantic evaluation of body shapes in obesity surgery patients and a group of controls.
Materials and Methods:
Thirty-four obesity surgery (OS) subjects, stable after weight loss (average 48.03 ± 18.60 kg), and 35 overweight/obese controls (MC), were enrolled in this study. Body dissatisfaction, self-esteem, and body perception were evaluated with self-reported tests, and semantic evaluation of body shapes was performed with three specific tasks constructed with realistic human body stimuli.
Results:
The OS showed a more positive body image compared to HC (p < 0.001), higher levels of depression (p < 0.019), and lower self-esteem (p < 0.000). OS patients and HC showed no difference in weight bias, but OS used a higher BMI than HC in the visualization of positive adjectives (p = 0.011). Both groups showed a mental underestimation of their body shapes.
Conclusion:
OS patients are more psychologically burdened and have more difficulties in judging their bodies than overweight/obese peers. Their mental body representations seem not to be linked to their own BMI. Our findings provide helpful insight for the design of specific interventions in body image in obese and overweight people, as well as in OS.
Considering cross-cultural context in the automatic recognition of emotions
Automatic recognition of emotions remains an ongoing challenge and much effort is being invested towards developing a system to solve this problem. Although several systems have been proposed, there is still none that considers the cultural context for emotion recognition. It remains unclear whether emotions are universal or culturally specific. A study on how culture influences the recognition of emotions is presented. For this purpose, a multicultural corpus for cross-cultural emotion analysis is constructed. Subjects from three different cultures—American, Asian and European—are recruited. The corpus is segmented and annotated. To avoid language artifacts, the emotion recognition model considers facial expressions, head movements, body motions and dimensional emotions. Three training and testing paradigms are carried out to compare cultural effects: intra-cultural, cross-cultural and multicultural emotion recognition. Intra-cultural and multicultural emotion recognition paradigms raised the best recognition results; cross-cultural emotion recognition rates were lower. These results suggest that emotion expression varies by culture, representing a hint of emotion specificity.
Towards developing robust multimodal databases for emotion analysis
Understanding emotions can make the difference between succeeding and failing during communication. Several systems have been developed in the field of Affective Computing in order to understand emotions. Recently these systems focus into multimodal emotion recognition. The basis of each of these systems is emotion databases. Even though a lot of attention has been placed in capturing spontaneous emotion expressions, building an emotion database is a task with several challenges that are commonly neglected, namely: quality of the recordings, ground truth, multiple device recording, data labeling and context. In this paper we present a new spontaneous emotion database, with human-computer and human to human interactions. This database is composed by eight different synchronized signals, in four interaction tasks. Strategies on how to deal with emotion database construction challenges are explained in detail.
Red shape, blue shape : political ideology influences the social perception of body shape
Political elections have a profound impact on individuals and societies. Optimal voting is thought to be based on informed and deliberate decisions yet, it has been demonstrated that the outcomes of political elections are biased by the perception of candidates’ facial features and the stereotypical traits voters attribute to these. Interestingly, political identification changes the attribution of stereotypical traits from facial features. This study explores whether the perception of body shape elicits similar effects on political trait attribution and whether these associations can be visualized. In Experiment 1, ratings of 3D body shapes were used to model the relationship between perception of 3D body shape and the attribution of political traits such as ‘Republican’, ‘Democrat’, or ‘Leader’. This allowed analyzing and visualizing the mental representations of stereotypical 3D body shapes associated with each political trait. Experiment 2 was designed to test whether political identification of the raters affected the attribution of political traits to different types of body shapes. The results show that humans attribute political traits to the same body shapes differently depending on their own political preference. These findings show that our judgments of others are influenced by their body shape and our own political views. Such judgments have potential political and societal implications.
BABEL: Bodies, Action and Behavior with English Labels
Understanding the semantics of human movement – the what, how and why of the movement – is an important problem that requires datasets of human actions with semantic labels. Existing datasets take one of two approaches. Large-scale video datasets contain many action labels but do not contain ground-truth 3D human motion. Alternatively, motion-capture (mocap) datasets have precise body motions but are limited to a small number of actions. To address this, we present BABEL, a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of language labels for over 43 hours of mocap sequences from AMASS, containing over 250 unique actions. Each action label in BABEL is precisely aligned with the duration of the corresponding action in the mocap sequence. BABELalso allows overlap of multiple actions, that may each span different durations. This results in a total of over 66000 action segments. The dense annotations can be leveraged for tasks like action recognition, temporal localization, motion synthesis, etc. To demonstrate the value of BABEL as a benchmark, we evaluate the performance of models on 3D action recognition. We demonstrate that BABEL poses interesting learning challenges that are applicable to real-world scenarios, and can serve as a useful benchmark for progress in 3D action recognition. The dataset, baseline methods, and evaluation code are available and supported for academic research purposes at https://babel.is.tue.mpg.de/.
Cultural dimension in emotion recognition for human machine interaction
Human emotion recognition is a multidimensional task. In this paper we study the effect of the cultural dimension in emotion recognition models and their use in human computer interaction. We prepared two experiments to analyze the consequences of disregarding culture in emotion recognition models. The results show that failing to consider the user's culture while applying emotion recognition techniques in interaction scenarios decreases the system's performance, making the emotional input meaningless and detrimental to the system and interaction.
Weight bias and linguistic body representation in anorexia nervosa : Findings from the BodyTalk project
Objective
This study provides a comprehensive assessment of own body representation and linguistic representation of bodies in general in women with typical and atypical anorexia nervosa (AN).
Methods
In a series of desktop experiments, participants rated a set of adjectives according to their match with a series of computer generated bodies varying in body mass index, and generated prototypic body shapes for the same set of adjectives. We analysed how body mass index of the bodies was associated with positive or negative valence of the adjectives in the different groups. Further, body image and own body perception were assessed.
Results
In a German‐Italian sample comprising 39 women with AN, 20 women with atypical AN and 40 age matched control participants, we observed effects indicative of weight stigmatization, but no significant differences between the groups. Generally, positive adjectives were associated with lean bodies, whereas negative adjectives were associated with obese bodies.
Discussion
Our observations suggest that patients with both typical and atypical AN affectively and visually represent body descriptions not differently from healthy women. We conclude that overvaluation of low body weight and fear of weight gain cannot be explained by generally distorted perception or cognition, but require individual consideration.
Visual appearance modulates motor control in social interactions
The goal of new adaptive technologies is to allow humans to interact with technical devices, such as robots, in natural ways akin to human interaction. Essential for achieving this goal, is the understanding of the factors that support natural interaction. Here, we examined whether human motor control is linked to the visual appearance of the interaction partner. Motor control theories consider kinematic-related information but not visual appearance as important for the control of motor movements (Flash & Hogan, 1985; Harris & Wolpert, 1998; Viviani & Terzuolo, 1982). We investigated the sensitivity of motor control to visual appearance during the execution of a social interaction, i.e. a high-five. In a novel mixed reality setup participants executed a high-five with a three-dimensional life-size human- or a robot-looking avatar. Our results demonstrate that movement trajectories and adjustments to perturbations depended on the visual appearance of the avatar despite both avatars carrying out identical movements. Moreover, two well-known motor theories (minimum jerk, two-thirds power law) better predict robot than human interaction trajectories. The dependence of motor control on the human likeness of the interaction partner suggests that different motor control principles might be at work in object and human directed interactions.
A Spontaneous Cross-Cultural Emotion Database : Latin-America vs. Japan
In this paper; we present a new database to support the cross-cultural studies. Two cultural groups are selected: Latin America and Japan; to represent western and oriental cultures. Emotions are elicited through an experiment in which participants observe emotionally loaded stimuli and then rate their feelings in a valence (how positive or negative is the experienced emotion) and arousal (how intense is this emotion) scale. The interactions are recorded using audiovisual and thermal devices. This database features three innovative characteristics: spontaneous emotion expressions; multiple synchronized sources of interaction; cross-cultural comparison support. This set of characteristics is missing in the currently available emotion databases; making our database a unique open option for studying spontaneous expressiveness of emotions in a cross-cultural context.