{"title":"Mem-Box: VR sandbox for adaptive working memory evaluation and training using physiological signals","authors":"Anqi Chen, Ming Li, Yang Gao","doi":"10.1007/s00371-024-03539-4","DOIUrl":null,"url":null,"abstract":"<p>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.\n</p>","PeriodicalId":501186,"journal":{"name":"The Visual Computer","volume":"16 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Visual Computer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s00371-024-03539-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.