A dual-task-embedded virtual reality system for intelligent quantitative assessment of cognitive processing speed

Yuzhao Zhou; Yixuan Zhao; Zirui Xiang; Zhixin Yan; Lin Shu; Xiangmin Xu; Lulu Zhang; Xiang Tian

doi:10.3389/fnhum.2023.1158650

A dual-task-embedded virtual reality system for intelligent quantitative assessment of cognitive processing speed

Front Hum Neurosci. 2023 Mar 30:17:1158650. doi: 10.3389/fnhum.2023.1158650. eCollection 2023.

Authors

Yuzhao Zhou¹, Yixuan Zhao¹, Zirui Xiang¹, Zhixin Yan², Lin Shu^{1

2

3}, Xiangmin Xu^{1

2

4}, Lulu Zhang⁵, Xiang Tian¹

Affiliations

¹ School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
² School of Future Technology, South China University of Technology, Guangzhou, China.
³ Pazhou Lab, Guangzhou, China.
⁴ Zhongshan Institute of Modern Industrial Technology of South China, University of Technology, Zhongshan, China.
⁵ Department of Psychiatry, Guangzhou First People's Hospital, The Second Affiliated Hospital, South China University of Technology, Guangzhou, China.

Abstract

Introduction: Processing speed is defined as the ability to quickly process information, which is generally considered as one of the affected cognitive functions of multiple sclerosis and schizophrenia. Paper-pencil type tests are traditionally used in the assessment of processing speed. However, these tests generally need to be conducted under the guidance of clinicians in a specific environment, which limits their application in cognitive assessment or training in daily life. Therefore, this paper proposed an intelligent evaluation method of processing speed to assist clinicians in diagnosis.

Methods: We created an immersive virtual street embedded with Stroop task (VR-Street). The behavior and performance information was obtained by performing the dual-task of street-crossing and Stroop, and a 50-participant dataset was established with the label of standard scale. Utilizing Pearson correlation coefficient to find the relationship between the dual-task features and the cognitive test results, and an intelligent evaluation model was developed using machine learning.

Results: Statistical analysis showed that all Stroop task features were correlated with cognitive test results, and some behavior features also showed correlation. The estimated results showed that the proposed method can estimate the processing speed score with an adequate accuracy (mean absolute error of 0.800, relative accuracy of 0.916 and correlation coefficient of 0.804). The combination of Stroop features and behavior features showed better performance than single task features.

Discussion: The results of this work indicates that the dual-task design in this study better mobilizes participants' attention and cognitive resources, and more fully reflects participants' cognitive processing speed. The proposed method provides a new opportunity for accurate quantitative evaluation of cognitive function through virtual reality.

Keywords: behavior data; cognitive processing speed; dual-task; evaluation; machine learning; virtual reality.

Grants and funding

This work was supported in part by the Major Science and Technology Projects in Guangdong Province under Grant no. 2016B010108008, the Technology Program of Guangzhou under Grant nos. 202002030354 and 202002030262, the Science and Technology Project of Zhongshan under Grant nos. 2019AG024 and 2020B2053, the Natural Science Foundation of Guangdong Province under Grant no. 2018A030310407, the Guangzhou Key Laboratory of Body Data Science under Grant no. 201605030011, and the Guangdong Provincial Key Laboratory of Human Digital Twin under Grant no. 2022B1212010004.