Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India.
Department of Industrial Engineering, University of Trento, 38123 Trento, Italy.
Sensors (Basel). 2023 Aug 1;23(15):6848. doi: 10.3390/s23156848.
The growing number of people with cognitive impairment will significantly increase healthcare demand. Screening tools are crucial for detecting cognitive impairment due to a shortage of mental health experts aiming to improve the quality of life for those living with this condition. Eye tracking is a powerful tool that can provide deeper insights into human behavior and inner cognitive processes. The proposed Eye-Tracking-Based Trail-Making Test, ETMT, is a screening tool for monitoring a person's cognitive function. The proposed system utilizes a fuzzy-inference system as an integral part of its framework to calculate comprehensive scores assessing visual search speed and focused attention. By employing an adaptive neuro-fuzzy-inference system, the tool provides an overall cognitive-impairment score, allowing psychologists to assess and quantify the extent of cognitive decline or impairment in their patients. The ETMT model offers a comprehensive understanding of cognitive abilities and identifies potential deficits in various domains. The results indicate that the ETMT model is a potential tool for evaluating cognitive impairment and can capture significant changes in eye movement behavior associated with cognitive impairment. It provides a convenient and affordable diagnosis, prioritizing healthcare resources for severe conditions while enhancing feedback to practitioners.
认知障碍人数的不断增加将显著增加医疗保健需求。由于缺乏旨在改善此类人群生活质量的心理健康专家，筛查工具对于检测认知障碍至关重要。眼动追踪是一种强大的工具，可以更深入地了解人类行为和内在认知过程。拟议的基于眼动追踪的连线测试（ETMT）是一种用于监测个体认知功能的筛查工具。该系统利用模糊推理系统作为其框架的一个组成部分，计算综合分数来评估视觉搜索速度和集中注意力。通过使用自适应神经模糊推理系统，该工具提供整体认知障碍评分，使心理学家能够评估和量化患者的认知下降或障碍程度。ETMT 模型提供了对认知能力的全面理解，并确定了各个领域的潜在缺陷。研究结果表明，ETMT 模型是评估认知障碍的一种潜在工具，可以捕捉与认知障碍相关的眼动行为的显著变化。它提供了一种便捷且经济实惠的诊断方法，优先考虑严重疾病的医疗保健资源，同时增强对从业者的反馈。
