Title
Virtual reality interfaces and population-specific models to mitigate public speaking anxiety
Abstract
Public speaking is key to effectively exchanging ideas, persuading others, and making a tangible impact. Yet, public speaking anxiety (PSA) ranks as a top social phobia among many people. This paper leverages bio-behavioural indices captured from wearable devices and virtual reality (VR) interfaces to quantify PSA. The significance of individual-specific factors, such as general trait anxiety and personality, as well as contextual factors, such as age, gender, highest education, and native language, in moderating the association between bio-behavioral indices and PSA is further examined through group-based machine learning models. Results highlight the importance of including such factors for detecting PSA with the proposed group-based PSA models yielding Spearman's correlation of 0.55(p <; 0.05) between the actual and predicted state-based anxiety scores. This work further analyzes whether systematic exposure to public speaking tasks in the VR environment can help alleviate PSA. Results indicate that systematic exposure to public speaking in VR can alleviate PSA in terms of both self-reported (p <; 0.05) and physiological (p <; 0.05) indices. Findings of this study will enable researchers to better understand antedecedents and causes of PSA contributing to behavioral interventions using VR.
Year
DOI
Venue
2019
10.1109/ACII.2019.8925509
2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)
Keywords
Field
DocType
public speaking anxiety,virtual reality,physiological signals,speech,wearable devices,group-based clustering
Social psychology,Population,Psychological intervention,Virtual reality,Computer science,Anxiety,Public speaking,Wearable technology,Applied psychology,First language,Personality
Conference
ISSN
ISBN
Citations 
2156-8103
978-1-7281-3889-3
0
PageRank 
References 
Authors
0.34
6
5
Name
Order
Citations
PageRank
Megha Yadav111.36
Md. Nazmus Sakib200.34
Kexin Feng323.53
Theodora Chaspari43819.43
Amir H. Behzadan512217.55