Smart driving technology for non-invasive detection of age-related cognitive decline

痴呆和其他年龄相关的神经退行性疾病，如阿尔茨海默病（AD）和阿尔茨海默病 疾病相关性痴呆（ADRD）导致患者生活质量显著下降， 对于65岁以上的成年人来说，这是死亡的主要原因。因此，更好地了解 痴呆症对于开发和评估有效的治疗方案至关重要。尽管有严峻的瞭望， 临床研究表明，在不可逆的脑损伤发生之前干预疾病是 改善AD/ADRD的结果。具体地说，轻度认知障碍（MCI），即脑卒中发作前的阶段。 痴呆症，可以为生活方式干预提供最大的“机会之窗”，可以延迟/预防MCI 和痴呆症。我们的团队报告了生活方式的改变，这为疾病带来了机会之窗 在不可逆的脑损伤之前进行治疗。 一些研究已经证明了MCI和驾驶性能之间的关系，包括驾驶 行为。虽然驾驶特征提供了一个无障碍的机会窗口来测试大脑 在日常认知挑战下的反应，目前是不切实际的昂贵和劳动密集型的， 广泛部署。我们假设一个无障碍的，连续的，经济的系统能够评估 可以检测神经退行性疾病早期阶段的驾驶特征将提供一个窗口， AD和ADRD。在这项工作中，我们建议开发和验证一个不引人注目的和汽车制造不可知的 传感系统，命名为智能垫，用于日常评估驾驶指标下的自由生活条件。的 系统：1）使用集成的生物传感器阵列和移动的App自动记录和分析数据， 评估驾驶; 2）使用生物传感器阵列不仅检测驾驶习惯，还检测新驾驶 性能指标和前所未有的驾驶员生物标志物（姿势和代谢率）， 与有害的认知阶段相关，以及3）由人工智能（AI）算法授权， 预测认知表现和MCI。在第一阶段的提案中，我们将利用Smart Pad原型 设计，我们已经开发和优化它的诊断驾驶员的认知状态。 TF Health Co. ASU-BNI团队将共同努力构建不引人注目的基于车辆的传感器 用于日常评估驾驶员表现和生物特征以及与年龄相关的认知衰退的系统， 开发新技术，用于在自由生活条件下早期诊断痴呆症，寻求改善患者护理 选择和促进认知能力下降预防干预措施。

Dementia and other age-related neurodegenerative diseases such as Alzheimer’s Disease (AD) and Alzheimer’s Disease Related Dementias (ADRD) cause a marked decrease in quality of life for patients, an increased burden of care, and, for adults over 65, are a leading cause of death. Therefore, better understanding of the nature of dementia is vital to the development and assessment of effective treatment options. Despite the grim lookout, clinical research suggests that intervening the disease before irreversible brain damage occurs is a key step to improve the outcomes of AD/ADRD. Specifically, Mild Cognitive Impairment (MCI), the stage before the onset of dementia, could provide the largest “window of opportunity” for lifestyle interventions that can delay/prevent MCI and dementia. Our team has reported lifestyle changes which brings a window of opportunity for the disease treatment prior to irreversible brain damages. Several studies have demonstrated a relationship between MCI and driving performance including driving behaviors. Although driving features provide an unobstructive window of opportunity to test the brain responses under everyday cognitive challenges, it is currently unrealistically costly and labor-intensive to be widely deployed. We hypothesize an unobstructive, continuous, economical system capable of assessing driving signatures that can detect early stages of neurodegenerative diseases would offer a window to reverse AD and ADRD. In this work, we propose to develop and validate an unobtrusive and car-make agnostic sensing system, named Smart Pad, for daily assessment of driving indicators under free-living conditions. The system: 1) automatically records and analyzes data with the integrated biosensor array and a mobile App to evaluate driving; 2) uses the biosensors array to detect not only driving habits, but also new driving performance indicators and unprecedented driver’s biomarker (posture and metabolic rate), which are correlated with detrimental cognitive stages, and 3) is empowered by an Artificial Intelligence (AI) algorithm to predict cognitive performance and MCI. In this Phase I proposal, we will leverage the Smart Pad prototype design that we have developed and optimize it to for the diagnosis of driver’s cognitive status. The TF Health Co.-ASU-BNI team will work together to build the unobtrusive, vehicle-based sensing system for daily assessment of driver performance and biometrics, and age-related cognitive decline to develop new technology for early diagnosis of dementia in free-living condition, seeking to improve patient care options and promote cognitive decline prevention interventions.