A neuroergonomic approach to assessing motor performance in stroke patients using fNIRS and behavioral data. (May 2023)

• Record Type: Journal Article
• Authors: Bu, Lingguo; Qu, Jing; Zhao, Lei; Zhang, Yanjie; Wang, Yonghui
• Is Part Of: Applied ergonomics. Volume 109(2023)
• Publication Date: 2023-05
• Subjects: Neuroergonomic -- Stroke -- fNIRS -- Motor performance -- Rehabilitation; Human engineering -- Periodicals; 620.82
• DOI: 10.1016/j.apergo.2023.103979
• Languages: English
• ISSNs: 0003-6870

Abstract

Abstract: Stroke is characterized by high morbidity and disability, and proposing effective methods for assessing and designing rehabilitation products is an attractive topic in current research. In this study, a hand function rehabilitation aid was developed for stroke patients. Ten stroke patients and 20 healthy older people as a control group were recruited to perform a 600 s task after a 600 s resting by gripping a stick while clicking on a flashing light in an electronic insert in sequence according to a pattern. The functional near-infrared spectroscopy (fNIRS) and behavioral data were collected during their rehabilitation training. Brain function was analyzed using three indicators, namely brain area activation, functional connectivity and effective connectivity, while behavioral performance was analyzed using ten indicators, such as velocity and acceleration, and correlations were made between both. Followed by proposing a quantitative assessment method based on the fusion of multiple data sources. The results showed that the developed rehabilitation tool could effectively stimulate the patient's brain and help recover their cognitive and behavioral capacities. The scientific validity of the proposed assessment approach was further confirmed by contrasting the data results of the stroke group with those of the healthy elderly group. This study has integrated brain function and behavioral data, providing a practical quantitative evaluation method of product ergonomics and data-driven product design concepts for stroke patients. Highlights: Developing a hand aid for recovering stroke patient using data-driven product design concepts. Proposing a multi-source data assessment method by integrating fNIRS and behavioral data. Revealing modifications in brain function and behavioral capacity between stroke patients and healthy elderly.