Autonomous Unobtrusive Detection of Mild Cognitive Impairment in Older Adults

The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adult population. We implement a signal processing approach equipped with a machine learning paradigm to process and analyze real-world data acquired using home-based unobtrusive sensing technologies. Using the sensor and clinical data pertaining to 97 subjects, acquired over an average period of three years, a number of measures associated with the subjects' walking speed and general activity in the home were calculated. Different time spans of these measures were used to generate feature vectors to train and test two machine learning algorithms namely support vector machines and random forests. We were able to autonomously detect MCI in older adults with an area under the ROC curve of 0.97 and an area under the precision-recall curve of 0.93 using a time window of 24 weeks. This study is of great significance since it can potentially assist in the early detection of cognitive impairment in older adults.

[1]  Andre Gustavo Adami,et al.  Unobtrusive assessment of activity patterns associated with mild cognitive impairment , 2008, Alzheimer's & Dementia.

[2]  Prafulla N. Dawadi,et al.  An approach to cognitive assessment in smart home , 2011, DMMH '11.

[3]  J. Kaye,et al.  Motor slowing precedes cognitive impairment in the oldest old , 1998, Neurology.

[4]  Misha Pavel,et al.  Unobtrusive and Ubiquitous In-Home Monitoring: A Methodology for Continuous Assessment of Gait Velocity in Elders , 2010, IEEE Transactions on Biomedical Engineering.

[5]  Barry Brumitt,et al.  EasyLiving: Technologies for Intelligent Environments , 2000, HUC.

[6]  Xiaonan Xue,et al.  Gait Dysfunction in Mild Cognitive Impairment Syndromes , 2008, Journal of the American Geriatrics Society.

[7]  Marjorie Skubic,et al.  Testing Classifiers for Embedded Health Assessment , 2012, ICOST.

[8]  Eric Dishman,et al.  Inventing wellness systems for aging in place , 2004, Computer.

[9]  A M Clarfield,et al.  The reversible dementias: do they reverse? , 1988, Annals of internal medicine.

[10]  T. Hayes,et al.  One walk a year to 1000 within a year: continuous in-home unobtrusive gait assessment of older adults. , 2012, Gait & posture.

[11]  J. Cummings,et al.  The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment , 2005, Journal of the American Geriatrics Society.

[12]  L. Boise,et al.  Diagnosing dementia: perspectives of primary care physicians. , 1999, The Gerontologist.

[13]  Maureen Schmitter-Edgecombe,et al.  Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  Benoit Mulsant,et al.  Detection and Management of Cognitive Impairment in Primary Care: The Steel Valley Seniors Survey , 2004, Journal of the American Geriatrics Society.

[15]  John H. Wasson,et al.  How do physicians diagnose dementia? Evidence from clinical vignette responses , 1997 .

[16]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[17]  J. Morris.  The Clinical Dementia Rating (CDR) , 1993, Neurology.

[18]  Gregory D. Abowd,et al.  Living laboratories: the future computing environments group at the Georgia Institute of Technology , 2000, CHI Extended Abstracts.

[19]  M. Pavel,et al.  Intelligent Systems For Assessing Aging Changes: home-based, unobtrusive, and continuous assessment of aging. , 2011, The journals of gerontology. Series B, Psychological sciences and social sciences.

[20]  Daniel Austin,et al.  In-home walking speeds and variability trajectories associated with mild cognitive impairment , 2012, Neurology.

[21]  Chris D. Nugent,et al.  Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management , 2012 .

[22]  David A Bennett,et al.  Motor dysfunction in mild cognitive impairment and the risk of incident Alzheimer disease. , 2006, Archives of neurology.

[23]  Dorothy Ndedi Monekosso,et al.  Behavior Analysis for Assisted Living , 2010, IEEE Transactions on Automation Science and Engineering.

[24]  Jeffrey Kaye,et al.  The trajectory of gait speed preceding mild cognitive impairment. , 2010, Archives of neurology.

[25]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[26]  William C. Mann,et al.  The Gator Tech Smart House: a programmable pervasive space , 2005, Computer.

[27]  Jeffrey Kaye,et al.  THE NEUROLOGY OF AGING , 2001 .

[28]  Diane J. Cook,et al.  Health Monitoring and Assistance to Support Aging in Place , 2006, J. Univers. Comput. Sci..