2,785 research outputs found

    Multi-modal Approach for Affective Computing

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    Throughout the past decade, many studies have classified human emotions using only a single sensing modality such as face video, electroencephalogram (EEG), electrocardiogram (ECG), galvanic skin response (GSR), etc. The results of these studies are constrained by the limitations of these modalities such as the absence of physiological biomarkers in the face-video analysis, poor spatial resolution in EEG, poor temporal resolution of the GSR etc. Scant research has been conducted to compare the merits of these modalities and understand how to best use them individually and jointly. Using multi-modal AMIGOS dataset, this study compares the performance of human emotion classification using multiple computational approaches applied to face videos and various bio-sensing modalities. Using a novel method for compensating physiological baseline we show an increase in the classification accuracy of various approaches that we use. Finally, we present a multi-modal emotion-classification approach in the domain of affective computing research.Comment: Published in IEEE 40th International Engineering in Medicine and Biology Conference (EMBC) 201

    Depression-related difficulties disengaging from negative faces are associated with sustained attention to negative feedback during social evaluation and predict stress recovery

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    The present study aimed to clarify: 1) the presence of depression-related attention bias related to a social stressor, 2) its association with depression-related attention biases as measured under standard conditions, and 3) their association with impaired stress recovery in depression. A sample of 39 participants reporting a broad range of depression levels completed a standard eye-tracking paradigm in which they had to engage/disengage their gaze with/from emotional faces. Participants then underwent a stress induction (i.e., giving a speech), in which their eye movements to false emotional feedback were measured, and stress reactivity and recovery were assessed. Depression level was associated with longer times to engage/disengage attention with/from negative faces under standard conditions and with sustained attention to negative feedback during the speech. These depression-related biases were associated and mediated the association between depression level and self-reported stress recovery, predicting lower recovery from stress after giving the speech

    Affective Umbrella – A Wearable System to Visualize Heart and Electrodermal Activity, towards Emotion Regulation through Somaesthetic Appreciation

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    In this paper, we introduce Affective Umbrella, a novel system to record, analyze and visualize physiological data in real time via an umbrella handle. We implement a biofeedback loop design in the system that triggers visualization changes to reflect and regulate emotions through somaesthetic appreciation. We report the methodology, processes, and results of data reliability and visual feedback impact on emotions. We evaluated the system using a real-life user study (n=21) in rainy weather at night. The statistical results demonstrate the potential of applying the visualization of biofeedback to regulate emotional arousal with a significantly higher (p=.0022) score, a lower (p=.0277) dominance than baseline from self-reported SAM Scale, and physiological arousal, which was shown to be significantly increased (p<.0001) with biofeedback in terms of pNN50 and a significant difference in terms of RMSSD. There was no significant difference in terms of emotional valence changes from SAM scale. Furthermore, we compared the difference between two biofeedback patterns (mirror and inversion). The mirror effect was with a significantly higher emotional arousal than the inversion effect (p=.0277) from SAM results and was with a significantly lower RMSSD performance than the inversion effect (p<.0001). This work demonstrates the potential for capturing physiological data using an umbrella handle and using this data to influence a user’s emotional state via lighting effects

    An Exploration into the Relationship between Indices of Autonomic Nervous System Health and Wellness

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    The maintenance and promotion of wellness proves to be vital to health. Over the years, existing literature has de-emphasized the contributions of objective health to the phenomenon of wellness, and has emphasized subjectively measured wellness concepts. However, due to the complexity of wellness and its importance in regard to individual and societal health, it is imperative to examine wellness not only from a subjective basis, but also in conjunction with objective explorations. A uniform index of wellness should be established in order to reduce the ambiguity associated with the concept. Therefore, this paper had two major aims that were addressed in three experiments testing college students’ self-report and physiological responses. Aim 1 was to develop a wellness model useful in a wide array of research domains. This was done through rigorous testing of components of my proposed Oliver Health Factor Wellness. Aim 2 was to establish an objective measure of wellness. This was done by correlating subjective wellness responses to wellness measures with objective physiological activity indicative of health. More specifically, I assessed Autonomic Nervous System (ANS) function as a means to explore the health and wellness status of individuals. In this paper, I addressed these aims and posit that my findings will advance scientific knowledge regarding a more steadfast way to measure wellness from an objective standpoint, as well as, a way to evaluate the efficacy of a given therapy by examination of changes in function/autonomic balance. In addition, my findings suggest a more reliable way to measure wellness, specifically, with its inclusion of ANS parameters. Finally, my findings suggest that Heart Rate Variability, in particular, can be utilized as an objective index of Holistic wellness and Optimum Health

    AffectiveViz:Designing Collective Stress Related Visualization

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    Higher heart rate variability predicts better affective interaction quality in non-intimate social interactions

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    Adaptive emotional responding is crucial for psychological well-being and the quality of social interactions. Resting heart rate variability (HRV), a measure of autonomic nervous system activity, has been suggested to index individual differences in emotion regulation (ER). As non-intimate social interactions require more regulatory efforts than intimate social interactions, we predicted that the association between HRV and affective interaction quality is moderated by the perceived intimacy of the exchange. Thus, we expected higher HRV to be particularly beneficial for affective interaction quality in non-intimate social interactions. Resting HRV was measured in the laboratory (N = 144). Subsequently, participants reported their affective interaction quality—as indicated by more positive and fewer negative emotions perceived in the self and the other—during an experience-sampling social interaction diary task. As predicted, in non-intimate interactions, individuals with higher HRV reported more positive and fewer negative emotions and perceived fewer negative emotions in their interaction partners. The results provide further insights into the relationship between HRV and emotional experiences during social interactions

    An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

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    Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use
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