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BACKGROUND Facial expressions are critical for conveying emotions and facilitating social interaction. Yet, little is known about how accurately sighted individuals recognize emotions facially expressed by people with visual impairments in online communication settings. OBJECTIVE This study aimed to investigate sighted individuals’ ability to understand facial expressions of six basic emotions in people with visual impairments during Zoom calls. It also aimed to examine whether education on facial expressions specific to people with visual impairments would improve emotion recognition accuracy. METHODS Sighted participants viewed video clips of individuals with visual impairments displaying facial expressions. They then identified the emotions displayed. Next, they received an educational session on facial expressions specific to people with visual impairments, addressing unique characteristics and potential misinterpretations. After education, participants viewed another set of video clips and again identified the emotions displayed. RESULTS Before education, participants frequently misidentified emotions. After education, their accuracy in recognizing emotions improved significantly. CONCLUSIONS This study provides evidence that education on facial expressions of people with visual impairments can significantly enhance sighted individuals’ ability to accurately recognize emotions in online settings. This improved accuracy has the potential to foster more inclusive and effective online interactions between people with and without visual disabilities.
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Exploring an Affective and Responsive Virtual Environment to Improve Remote Learning
Online classes are typically conducted by using video conferencing software such as Zoom, Microsoft Teams, and Google Meet. Research has identified drawbacks of online learning, such as “Zoom fatigue”, characterized by distractions and lack of engagement. This study presents the CUNY Affective and Responsive Virtual Environment (CARVE) Hub, a novel virtual reality hub that uses a facial emotion classification model to generate emojis for affective and informal responsive interaction in a 3D virtual classroom setting. A web-based machine learning model is employed for facial emotion classification, enabling students to communicate four basic emotions live through automated web camera capture in a virtual classroom without activating their cameras. The experiment is conducted in undergraduate classes on both Zoom and CARVE, and the results of a survey indicate that students have a positive perception of interactions in the proposed virtual classroom compared with Zoom. Correlations between automated emojis and interactions are also observed. This study discusses potential explanations for the improved interactions, including a decrease in pressure on students when they are not showing faces. In addition, video panels in traditional remote classrooms may be useful for communication but not for interaction. Students favor features in virtual reality, such as spatial audio and the ability to move around, with collaboration being identified as the most helpful feature.
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- PAR ID:
- 10440665
- Date Published:
- Journal Name:
- Virtual Worlds
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2813-2084
- Page Range / eLocation ID:
- 53 to 74
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/virtualworlds2010004
