LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/1/86 |
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| author | Firas Al-Hindawi Peter Serhan Yonas E. Geda Francis Tsow Teresa Wu Erica Forzani |
| author_facet | Firas Al-Hindawi Peter Serhan Yonas E. Geda Francis Tsow Teresa Wu Erica Forzani |
| author_sort | Firas Al-Hindawi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d27e1673ef09485e95b4aac753c7e986 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-d27e1673ef09485e95b4aac753c7e9862025-01-24T13:23:13ZengMDPI AGBioengineering2306-53542025-01-011218610.3390/bioengineering12010086LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive ImpairmentFiras Al-Hindawi0Peter Serhan1Yonas E. Geda2Francis Tsow3Teresa Wu4Erica Forzani5School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USASchool of Electrical, Computer and Energy Engineering, Tempe, AZ 85281, USABarrow Neurological Institute, 2910 N 3rd Ave, Phoenix, AZ 85013, USATF Health Corporation (DBA Breezing Co.), 6161 E. Mayo Blvd., Phoenix, AZ 85054, USASchool of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USACenter for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, 1001 S McAllister Ave, Tempe, AZ 85281, USAAlzheimer’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.https://www.mdpi.com/2306-5354/12/1/86Alzheimer’s diseasemild cognitive impairmentsmart drivingmachine learning |
| spellingShingle | Firas Al-Hindawi Peter Serhan Yonas E. Geda Francis Tsow Teresa Wu Erica Forzani LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment Bioengineering Alzheimer’s disease mild cognitive impairment smart driving machine learning |
| title | LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment |
| title_full | LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment |
| title_fullStr | LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment |
| title_full_unstemmed | LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment |
| title_short | LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment |
| title_sort | livedrive ai a pilot study of a machine learning powered diagnostic system for real time non invasive detection of mild cognitive impairment |
| topic | Alzheimer’s disease mild cognitive impairment smart driving machine learning |
| url | https://www.mdpi.com/2306-5354/12/1/86 |
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