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

用于无创检测与年龄相关的认知衰退的智能驾驶技术

• 批准号: 10484798
• 负责人: Erica Silvia Forzani
• 金额: $ 45万
• 依托单位: TF HEALTH CORPORATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10484798
• 关键词: Adult; Age; Age-associated memory impairment; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Attention; Automobile Driving; Award; Biological Markers; Biometry; Biosensor; Brain; Brain Injuries; Brain region; Calibration; Cause of Death; Clinical; Clinical Research; Cognition; Cognitive; Collection; Data Analyses; Decision Making; Dementia; Development; Diagnosis; Disease; Early Diagnosis; Elderly; Energy Intake; Entrepreneurship; Event; Exercise; Experimental Designs; Funding; Future; General Population; Habits; Health; Health Professional; Impaired cognition; Institutes; Knowledge; Learning; Life Style; Life Style Modification; Literature; Modeling; Names; Nature; Neurodegenerative Disorders; Neurologic; Non-Invasive Cancer Detection; Patient Care; Patients; Performance; Phase; Physical Exercise; Posture; Prevention; Psyche structure; Quality of life; Records; Reporting; Research; Risk; Risk Assessment; Standardization; System; Technology; Testing; Time; Validation; Work; age related neurodegeneration; artificial intelligence algorithm; base; care burden; cognitive function; cognitive performance; cost; deep learning model; design; diagnostic accuracy; driving behavior; effective therapy; empowered; fighting; impaired driving performance; improved; improved outcome; learning algorithm; lifestyle intervention; metabolic rate; mild cognitive impairment; mobile application; multi-task learning; new technology; patient population; prevent; preventive intervention; professor; prototype; recruit; response; sensor; signal processing; tool

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.

痴呆症和其他与年龄相关的神经退行性疾病，例如阿尔茨海默病 (AD) 和阿尔茨海默病 疾病相关痴呆症（ADRD）导致患者生活质量显着下降，负担增加 对于 65 岁以上的成年人来说，护理是导致死亡的主要原因，因此，需要更好地了解护理的本质。 尽管前景严峻，但痴呆症对于制定和评估有效的治疗方案至关重要。 临床研究表明，在不可逆的脑损伤发生之前干预疾病是治疗疾病的关键一步。 改善 AD/ADRD 的结果，特别是轻度认知障碍 (MCI)，即 AD 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”，从而延缓/预防 MCI 我们的团队报告说，生活方式的改变为这种疾病带来了机会。 在不可逆的脑损伤之前进行治疗。 多项研究表明 MCI 与驾驶表现（包括驾驶）之间存在关系 尽管驾驶功能提供了一个无障碍的测试大脑的机会窗口。 日常认知挑战下的反应，目前的成本高昂且劳动密集型，不切实际。 我们追求一种能够评估的无障碍、连续、经济的系统。 可以检测神经退行性疾病早期阶段的驱动特征将为逆转提供一个窗口 在这项工作中，我们建议开发和验证一个不显眼且与汽车制造无关的系统。 名为Smart Pad的传感系统，用于在自由生活条件下日常评估驾驶指标。 系统：1）通过集成生物传感器阵列和移动应用程序自动记录和嵌套数据 评估驾驶；2）使用生物传感器阵列不仅可以检测驾驶习惯，还可以检测新的驾驶方式 性能指标和前所未有的驾驶员生物标志物（姿势和代谢率）， 与受损的认知阶段相关，并且 3) 由人工智能 (AI) 算法授权 预测认知表现和 MCI 在第一阶段提案中，我们将利用 Smart Pad 原型。 我们开发并优化了它的设计，用于诊断驾驶员的认知状态。 TF Health Co.-ASU-BNI 团队将共同打造低调的车载传感系统 用于日常评估驾驶员表现和生物识别以及与年龄相关的认知能力下降的系统 开发在自由生活条件下早期诊断痴呆症的新技术，寻求改善患者护理 选择并促进认知能力下降预防干预措施。