Fig 3 - uploaded by David Antonio Gómez Jáuregui
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Normalized power spectrums of the COG from the video and the force plate and the COP from the force plate.
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Postural control is a dynamical process that has been extensively studied in motor control research. Recent experimental work shows a direct impact of affects on human balance. However, few studies on the automatic recognition of affects in full body expressions consider balance variables such as center of gravity displacements. Force plates enable...
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Sequence-specific postural motor learning in a target-directed weight-shifting task in 12 older and 12 young participants was assessed. In the implicit sequence learning condition participants performed a concurrent spatial cognitive task and in the two explicit conditions participants were required to discover the sequence order either with or wit...
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... The difference in stress felt between the real and virtual conditions is congruent with most of the differences in behaviors: being less stressed induces less controlled behaviors (more quantity of motion; center of pressure displacements that correspond to a more articulated mode of intersegmental functioning, freeing joint degrees of freedom). In fact, as negative or threat stimuli can trigger flee behaviors, fight behaviors or freezing behaviors 83 , a job interview is considered to be a situation that can induce freezing behaviors 19 . The increase in the contraction index for the virtual interviewer simulation is more difficult to interpret. ...
Postural interaction is of major importance during job interviews. While several prototypes enable users to rehearse for public speaking tasks and job interviews, few of these prototypes support subtle bodily interactions between the user and a virtual agent playing the role of an interviewer. The design of our system is informed by a multimodal corpus that was previously collected. In this paper, we explain how we were inspired by these video recordings of human interviewers to build a library of motion-captured movements that interviewers are most likely to display. We designed a fully automatic interactive virtual agent able to display these movements in response to the bodily movements of the user. Thirty-two participants presented themselves to this virtual agent during a simulated job interview. We focused on the self-presentation task of the job interview, the virtual agent being listening. Participants stood on a force platform that recorded the displacements of their center of pressure to assess the postural impact of our design. We also collected video recordings of their movements and computed the contraction index and the quantity of motion of their bodies. We explain the different hypotheses that we made concerning (1) the comparison between the performance of participants with human interviewers and the performance of participants with virtual interviewers, (2) the comparison between mirror and random postural behaviors displayed by a female versus a male virtual interviewer, and (3) the correlation between the participants' performance and their personality traits. Our results suggest that users perceive the simulated self-presentation task with the virtual interviewer as threatening and as difficult as the presentation task with the human interviewers. Furthermore, when users interact with a virtual interviewer that mirrors their postures, these users perceive the interviewer as being affiliative. Finally, a correlation analysis showed that personality traits had a significant relation to the postural behaviors and performance of the users during their presentation.
... The center of gravity (COG) displacement is an indicator of balance and postural control ( Fig. 3c and 3d). Higher COG displacements have been reported to be associated with negative emotions and stressful situation appraisals [35]. The COG in the horizontalvertical plane was extracted directly from the participant's silhouette using the following equations: ...
The aim of the present study is to identify relevant nonverbal features allowing the discrimination of different stressful behaviors, with the consideration of personality factors. In order to achieve this aim, we propose a new method for psychological stress induction involving four different stressful tasks. The proposed protocol was tested with 45 PhD students and the analysis of heart rate variability suggests that stress was indeed elicited. PhD students were selected as participants because they often experience stress. Multimodal data was collected and analyzed in order to identify nonverbal behavioral features related to the different stressful tasks. The psychological profile of participants was taken into account to understand how different stressful behaviors are correlated with personality factors. Results suggest that relevant nonverbal behaviors can discriminate between stressful tasks. In addition, relevant behaviors involving movement variability appear to be correlated with personality factors and stressful tasks.
... However, this has not been applied to automatic gait recognition. Initial work by Giraud et al. [50] showed the relationship between the changes in Centre of Gravity through video silhouettes, and the centre of gravity and centre of pressure on force plates to assess change in posture when reacting negatively and positively towards situations. Although this new method was not tested in automatic affect recognition, their data suggests it is a suitable alternative to required force pressure plates to analyse changes in pressure whilst walking. ...
There has been a growing interest in machine-based recognition of emotions from body gait and its combination with other modalities. In order to highlight the major trends and state of the art in this area, the literature dealing with machine-based human emotion perception through gait and posture is explored. Initially the effectiveness of human intellect and intuition in perceiving emotions in a range of cultures is examined. Subsequently, major studies in machine-based affect recognition are reviewed and their performance is compared. The survey concludes by critically analysing some of the issues raised in affect recognition using gait and posture, and identifying gaps in the current understanding in this area.
... In the literature, several sets of body features have been used to recognize or to synthesize someone's affective state [47], [48], [49]. Regarding automatic stress detection systems, only few of them actually use body features. ...
Stress is a complex phenomenon that impacts the body and the mind at several levels. It has been studied for more than a century from different perspectives, which result in different definitions and different ways to assess the presence of stress. This paper introduces a methodology for analyzing multimodal stress detection results by taking into account the variety of stress assessments. As a first step, we have collected video, depth and physiological data from 25 subjects in a stressful situation: a socially evaluated mental arithmetic test. As a second step, we have acquired 3 different assessments of stress: self-assessment, assessments from external observers and assessment from a physiology expert. Finally, we extract 101 behavioural and physiological features and evaluate their predictive power for the 3 collected assessments using a classification task. Using multimodal features, we obtain average F1 scores up to 0.85. By investigating the composition of the best selected feature subsets and the individual feature classification performances, we show that several features provide valuable information for the classification of the 3 assessments: features related to body movement, blood volume pulse and heart rate. From a methodological point of view, we argue that a multiple assessment approach provide more robust results.
... There is few research of how stress can affect our body language. However, in order to recognize or to regenerate someone's affective state, there are several sets of features extracted from the body that are usually used: the body activity [4], [13], [15], [20], posture information such as symmetry [13], [15], center of gravity displacements [14], or the spatial extent [4], [15], kinematics information such as smoothness [4], [15] and detection of specific gestures [13], [15]. ...
This paper introduces behavioural features for automatic stress detection, and a person-specific normalization to enhance the performance of our system. The presented features are all visual cues automatically extracted using video processing and depth data. In order to collect the necessary data, we conducted a lab study for stress elicitation using a time constrained arithmetic mental test. Then, we propose a set of body language features for stress detection. Experimental results using a SVM show that our model can detect stress with high accuracy (77%). Moreover, person specific normalization significantly improves classification results (from 67% to 77%). Also, the performance of each of the presented features is discussed.
