How Interpersonal Distance Between Avatar and Human Influences Facial Affect Recognition in Immersive Virtual Reality

Abstract

Purpose: The purpose of this study was to determine the optimal interpersonal distance (IPD) between humans and affective avatars in facial affect recognition in immersive virtual reality (IVR). The ideal IPD is the one in which the humans show the highest number of hits and the shortest reaction times in recognizing the emotions displayed by avatars. The results should help design future therapies to remedy facial affect recognition deficits.Methods: A group of 39 healthy volunteers participated in an experiment in which participants were shown 65 dynamic faces in IVR and had to identify six basic emotions plus neutral expression presented by the avatars. We decided to limit the experiment to five different distances: D1 (35 cm), D2 (55 cm), D3 (75 cm), D4 (95 cm), and D5 (115 cm), all belonging to the intimate and personal interpersonal spaces. Of the total of 65 faces, 13 faces were presented for each of the included distances. The views were shown at different angles: 50% in frontal view, 25% from the right profile, and 25% from the left profile. The order of appearance of the faces presented to each participant was randomized.Results: The overall success rate in facial emotion identification was 90.33%, being D3 the IPD with the best overall emotional recognition hits, although statistically significant differences could not be found between the IPDs. Consistent with results obtained in previous studies, identification rates for negative emotions were higher with increasing IPD, whereas the recognition task improved for positive emotions when IPD was closer. In addition, the study revealed irregular behavior in the facial detection of the emotion surprise.Conclusions: IVR allows us to reliably assess facial emotion recognition using dynamic avatars as all the IPDs tested showed to be effective. However, no statistically significant differences in facial emotion recognition were found among the different IPDs.

Virtual Reality Technology in Cognitive Rehabilitation Application: Bibliometric Analysis

“…Traditional therapeutic tools are limited to psychotherapy and drug therapy. The development of VR technology has promoted its application in this field, including a series of studies aimed at assessing and improving symptoms of schizophrenia [ 63 ], as well as VR emotion recognition tasks aimed at treating facial emotion recognition disorders in patients with neurological and psychiatric disorders in recent years [ 64 , 65 ]. To develop VR into a mature tool for improving mental health, researchers need to integrate knowledge from health care, computer science, neuroscience, psychology, social cognition, multisensory perception, and multimedia development in an interdisciplinary approach [ 27 ] and specifically combine user needs.…”

Could Avatar Therapy Enhance Mental Health in Chronic Patients? A Systematic Review

“…The use of avatars in treating illnesses has advanced in recent years, offering many possibilities in terms of the pathologies they are geared towards. Although not all the benefits of this kind of intervention are currently known, good results have been obtained in the treatment of anxiety in disorders such as agoraphobia [20] and social phobia [17][18][19], as well as in treating eating disorders [13,14], addictions [15,16], weight control [9][10][11], facial pattern recognition [26][27][28][29][30], and ASD [31][32][33]. Education is another area in which they have been used.…”

“…Facial emotion recognition is another area of study in which virtual reality has been used. The results found in the scientific literature suggest that immersive virtual reality, through the use of avatars, can be used as a tool for training patients in recognizing emotions [26]. This finding is particularly useful for people with schizophrenia, as they find it difficult to recognize facial emotions, causing alterations in their interpersonal relationships [27,28].…”

Understanding How CNNs Recognize Facial Expressions: A Case Study with LIME and CEM

“…Moreover, the use of synthetic datasets has proved to be a good substitution for real-image datasets, as they obtain recognition rates similar to the real ones [ 21 , 22 ].…”