Al-Hindawi Firas, Serhan Peter, Geda Yonas E, Tsow Francis, Wu Teresa, Forzani Erica
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA.
ASU Mayo Center for Innovative Imaging, Arizona State University, Tempe, AZ 85281, USA.
Bioengineering (Basel). 2025 Jan 17;12(1):86. doi: 10.3390/bioengineering12010086.
Alzheimer's disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities. Early detection, particularly of mild cognitive impairment (MCI) as an indicator of potential AD onset, is crucial yet challenging, given the limitations of current diagnostic biomarkers and the need for non-invasive, accessible tools. This study aims to address these gaps by exploring driving performance as a novel, non-invasive biomarker for MCI detection. Using the LiveDrive AI system, equipped with multimodal sensing (MMS) technology and a driving performance assessment strategy, the proposed work analyzes the predictive capacity of driving patterns in indicating cognitive decline. Machine learning models, trained on an expert-annotated in-house dataset, were employed to detect MCI status from driving performance. Key findings demonstrate the feasibility of using nuanced driving features, such as velocity and acceleration during turning, as indicators of cognitive decline. This approach holds promise for integration into smartphone or car applications, enabling real-time, continuous cognitive health monitoring. The implications of this work suggest a transformative step towards scalable, real-world solutions for early AD diagnosis, with the potential to improve patient outcomes and disease management.
阿尔茨海默病（AD）是一个重大的全球健康问题，全球有超过5500万人受其影响，对认知和功能能力产生渐进性影响。鉴于当前诊断生物标志物的局限性以及对非侵入性、易获取工具的需求，早期检测，尤其是将轻度认知障碍（MCI）作为潜在AD发病指标的检测，至关重要但具有挑战性。本研究旨在通过探索驾驶表现作为一种用于MCI检测的新型非侵入性生物标志物来填补这些空白。利用配备多模态传感（MMS）技术和驾驶表现评估策略的LiveDrive AI系统，本研究分析了驾驶模式在指示认知衰退方面的预测能力。基于内部专家标注数据集训练的机器学习模型被用于从驾驶表现中检测MCI状态。关键发现表明，使用细微的驾驶特征，如转弯时的速度和加速度，作为认知衰退指标是可行的。这种方法有望集成到智能手机或汽车应用中，实现实时、持续的认知健康监测。这项工作的意义在于朝着可扩展的、现实世界的早期AD诊断解决方案迈出了变革性的一步，有可能改善患者预后和疾病管理。
