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Assessing Virtual Reality Spaces for Elders Using Image-Based Sentiment Analysis and Stress Level Detection.

Makrina Viola Kosti¹, Nefeli Georgakopoulou¹, Sotiris Diplaris¹

¹Centre for Research and Technology Hellas, CERTH-ITI, 57001 Thermi, Thessaloniki, Greece.

Sensors (Basel, Switzerland)

|April 28, 2023

Summary

This summary is machine-generated.

Social virtual reality (VR) offers seniors new ways to connect and stay mentally active, combating loneliness and isolation. This study explores using visual sentiment and behavioral analysis to evaluate social VR spaces for older adults.

Keywords:

elders fusion sentiment stress virtual reality visual analysis

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Area of Science:

Gerontology
Human-Computer Interaction
Computer Science

Background:

Social isolation and loneliness are significant concerns for seniors, impacting their social capital and cognitive health.
Social virtual reality (VR) environments are emerging as a potential solution to mitigate social isolation in older adults.
Rigorous evaluation methods are crucial for assessing the efficacy and safety of VR interventions for this vulnerable population.

Purpose of the Study:

To introduce and evaluate the application of novel assessment techniques for social VR environments designed for seniors.
To explore the potential of image-based sentiment analysis and behavioral analysis in understanding user experience within social VR.
To provide preliminary insights into the effectiveness of social VR as a tool for enhancing social engagement among older adults.

Main Methods:

Utilized image-based sentiment analysis to gauge emotional responses within the social VR setting.
Employed behavioral analysis to observe and quantify user interactions and engagement patterns.
Focused on assessing a specifically designed social VR space tailored for elder users.

Main Results:

Preliminary results indicate that sentiment and behavioral analysis can effectively capture user experiences in social VR.
The study identified specific patterns of interaction and emotional expression within the VR environment.
Demonstrated the feasibility of using these analytical techniques for evaluating VR interventions for seniors.

Conclusions:

Image-based sentiment and behavioral analysis show promise as robust methods for evaluating social VR platforms for older adults.
These techniques can provide valuable data to inform the design and improvement of VR experiences aimed at reducing senior loneliness.
Further research is warranted to validate and expand upon these findings in diverse social VR contexts for the elderly.