Back to Search Start Over

Mem-Box: VR sandbox for adaptive working memory evaluation and training using physiological signals.

Authors :
Chen, Anqi
Li, Ming
Gao, Yang
Source :
Visual Computer. Nov2024, Vol. 40 Issue 11, p7559-7573. 15p.
Publication Year :
2024

Abstract

Working memory is crucial for higher cognitive functions in humans and is a focus in cognitive rehabilitation. Compared to conventional working memory training methods, VR-based training provides a more immersive experience with realistic scenarios, offering enhanced transferability to daily life. However, existing VR-based training methods often focus on basic cognitive tasks, underutilize VR's realism, and rely heavily on subjective assessment methods. In this paper, we introduce a VR Sandbox for working memory training and evaluation, MEM-Box, which simulates everyday life scenarios and routines and adaptively adjusts task difficulty based on user performance. We conducted a training experiment utilizing the MEM-Box and compared it with a control group undergoing PC-based training. The results of the Stroop test indicate that both groups demonstrated improvements in working memory abilities, with MEM-Box training showing greater efficacy. Physiological data confirmed the effectiveness of the MEM-Box, as we observed lower HRV and SDNN. Furthermore, the results of the frequency-domain analysis indicate higher sympathetic nervous system activity (LFpower and LF/HF) during MEM-Box training, which is related to the higher sense of presence in VR. These metrics pave the way for building adaptive VR systems based on physiological data. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01782789
Volume :
40
Issue :
11
Database :
Academic Search Index
Journal :
Visual Computer
Publication Type :
Academic Journal
Accession number :
180734101
Full Text :
https://doi.org/10.1007/s00371-024-03539-4