Smart Driving Technology for Non-Invasive Detection of Age-Related Cognitive Decline

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 24; p. 8062
• Main Authors: Serhan, Peter, Victor, Shaun, Osorio Perez, Oscar, Abi Karam, Kevin, Elghoul, Anthony, Ransdell, Madison, Al-Hindawi, Firas, Geda, Yonas, Chahal, Geetika, Eagan, Danielle, Wu, Teresa, Tsow, Francis, Forzani, Erica
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.12.2024
• Subjects: Accelerometers; Activities of daily living; Advertising executives; Aged; Algorithms; Alzheimer Disease - diagnosis; Alzheimer's disease; Alzheimer’s Disease (AD); Alzheimer’s early detection; Artificial Intelligence; Automobile Driving; Biosensing Techniques - instrumentation; Biosensing Techniques - methods; Carbon dioxide; Cognition & reasoning; Cognitive ability; Cognitive Dysfunction - diagnosis; cognitive health monitoring; Data collection; Decision making; Dementia; Disease; Energy intake; Exercise; Female; Health care; Humans; Indoor air quality; Machine learning; Male; Memory; Metabolism; Mild Cognitive Impairment (MCI); multi-modal sensing array (MMS); Nervous system diseases; Older people; Outdoor air quality; Pilot projects; Sensors; Smart Driving System; Ventilation; Serbia; United States; Arizona; United States > US; Alzheimer’s Disease (AD); Mild Cognitive Impairment (MCI); multi-modal sensing array (MMS); Smart Driving System; cognitive health monitoring; Alzheimer’s early detection
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24248062

Alzheimer’s disease (AD) and Alzheimer’s Related Dementias (ADRD) are projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window of opportunity for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating activities, can reduce the risk of MCI. Early detection of MCI is challenging due to subtle and often unnoticed cognitive decline and is traditionally monitored through infrequent clinical tests. In this research, the Smart Driving System, a novel, unobtrusive, and economical technology to detect early stages of neurodegenerative diseases, is presented. The system comprises a multi-modal biosensing array (MMS) and AI algorithms, including driving performance and driver’s biometrics, offering insights into a driver’s cognitive function. This publication is the first work reported towards the ultimate goal of developing the Smart Driving Device and App, integrating it into vehicles, and validating its effectiveness in detecting MCI through comprehensive pilot studies.