Predicting mild cognitive impairment through ambient sensing and artificial intelligence

This paper reports an emerging application leveraging ambient and artificial intelligence techniques for in-home sensing and cognitive health assessment. The application involves a prospective longitudinal study, wherein non-pervasive sensing devices are installed in homes of over 63 real users unde...

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Main Authors: TAN, Ah-hwee, YING, Weng Yan, SUBAGDJA, Budhitama, HUANG, Anni, D, Shanthoshigaa, TAY, Tony Chin-Ian, RAWTAER, Iris
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9277
https://ink.library.smu.edu.sg/context/sis_research/article/10277/viewcontent/MildCognitiveImpairment_CAI_2024_av.pdf
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spelling sg-smu-ink.sis_research-102772024-09-09T06:58:46Z Predicting mild cognitive impairment through ambient sensing and artificial intelligence TAN, Ah-hwee YING, Weng Yan SUBAGDJA, Budhitama HUANG, Anni D, Shanthoshigaa TAY, Tony Chin-Ian RAWTAER, Iris This paper reports an emerging application leveraging ambient and artificial intelligence techniques for in-home sensing and cognitive health assessment. The application involves a prospective longitudinal study, wherein non-pervasive sensing devices are installed in homes of over 63 real users undergoing clinical cognitive assessment, and digital signals of the users’ activities and behaviour are transmitted to a central cloud-based data server for further processing and analysis. Based on the sensor readings, we identify a set of digital biomarkers covering four key aspects of daily living, namely physical, activity, cognitive, and sleep, and develop a suite of customized feature extraction methods for deriving them from the sensor readings. As sensor data captured from real world are inherently sparse and noisy, we build predictive models using various machine learning techniques and evaluate their sensitivity to missing and noisy data. Validated with findings of clinical assessment, our experiments show that machine learning-based predictive models are able to identify mild cognitive impairment (MCI) cases based on the extracted digital biomarkers with reasonably high F1 scores of more than 0.85. This shows that the sensor-based digital biomarkers are indicative of the users’ cognitive health status and could be further exploited for more general health assessment applications. With a vision of massively deploying such sensor-based AI systems, the paper discusses the challenges we encountered and shares our lessons learned. 2024-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9277 info:doi/10.1109/CAI59869.2024.00198 https://ink.library.smu.edu.sg/context/sis_research/article/10277/viewcontent/MildCognitiveImpairment_CAI_2024_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University biomarker extraction mild cognitive impairment Predictive modelling Artificial Intelligence and Robotics Health Information Technology
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic biomarker extraction
mild cognitive impairment
Predictive modelling
Artificial Intelligence and Robotics
Health Information Technology
spellingShingle biomarker extraction
mild cognitive impairment
Predictive modelling
Artificial Intelligence and Robotics
Health Information Technology
TAN, Ah-hwee
YING, Weng Yan
SUBAGDJA, Budhitama
HUANG, Anni
D, Shanthoshigaa
TAY, Tony Chin-Ian
RAWTAER, Iris
Predicting mild cognitive impairment through ambient sensing and artificial intelligence
description This paper reports an emerging application leveraging ambient and artificial intelligence techniques for in-home sensing and cognitive health assessment. The application involves a prospective longitudinal study, wherein non-pervasive sensing devices are installed in homes of over 63 real users undergoing clinical cognitive assessment, and digital signals of the users’ activities and behaviour are transmitted to a central cloud-based data server for further processing and analysis. Based on the sensor readings, we identify a set of digital biomarkers covering four key aspects of daily living, namely physical, activity, cognitive, and sleep, and develop a suite of customized feature extraction methods for deriving them from the sensor readings. As sensor data captured from real world are inherently sparse and noisy, we build predictive models using various machine learning techniques and evaluate their sensitivity to missing and noisy data. Validated with findings of clinical assessment, our experiments show that machine learning-based predictive models are able to identify mild cognitive impairment (MCI) cases based on the extracted digital biomarkers with reasonably high F1 scores of more than 0.85. This shows that the sensor-based digital biomarkers are indicative of the users’ cognitive health status and could be further exploited for more general health assessment applications. With a vision of massively deploying such sensor-based AI systems, the paper discusses the challenges we encountered and shares our lessons learned.
format text
author TAN, Ah-hwee
YING, Weng Yan
SUBAGDJA, Budhitama
HUANG, Anni
D, Shanthoshigaa
TAY, Tony Chin-Ian
RAWTAER, Iris
author_facet TAN, Ah-hwee
YING, Weng Yan
SUBAGDJA, Budhitama
HUANG, Anni
D, Shanthoshigaa
TAY, Tony Chin-Ian
RAWTAER, Iris
author_sort TAN, Ah-hwee
title Predicting mild cognitive impairment through ambient sensing and artificial intelligence
title_short Predicting mild cognitive impairment through ambient sensing and artificial intelligence
title_full Predicting mild cognitive impairment through ambient sensing and artificial intelligence
title_fullStr Predicting mild cognitive impairment through ambient sensing and artificial intelligence
title_full_unstemmed Predicting mild cognitive impairment through ambient sensing and artificial intelligence
title_sort predicting mild cognitive impairment through ambient sensing and artificial intelligence
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9277
https://ink.library.smu.edu.sg/context/sis_research/article/10277/viewcontent/MildCognitiveImpairment_CAI_2024_av.pdf
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