TY - JOUR
T1 - Clustering home activity distributions for automatic detection of mild cognitive impairment in older adults 1
AU - Akl, Ahmad
AU - Chikhaoui, Belkacem
AU - Mattek, Nora
AU - Kaye, Jeffrey
AU - Austin, Daniel
AU - Mihailidis, Alex
N1 - Publisher Copyright:
© 2016 - IOS Press and the authors. All rights reserved.
PY - 2016
Y1 - 2016
N2 - The public health implications of growing numbers of older adults at risk for dementia places pressure on identifying dementia at its earliest stages so as to develop proactive management plans. The prodromal dementia phase commonly identified as mild cognitive impairment is an important target for this early detection of impending dementia amenable to treatment. In this paper, we propose a method for home-based automatic detection of mild cognitive impairment in older adults through continuous monitoring via unobtrusive sensing technologies. Our method is composed of two main stages: a training stage and a test stage. For training, room activity distributions are estimated for each subject using a time frame of ω weeks, and then affinity propagation is employed to cluster the activity distributions and to extract exemplars to represent the different emerging clusters. For testing, room activity distributions belonging to a test subject with unknown cognitive status are compared to the extracted exemplars and get assigned the labels of the exemplars that result in the smallest normalized Kullbak-Leibler divergence. The labels of the activity distributions are then used to determine the cognitive status of the test subject. Using the sensor and clinical data pertaining to 85 homes with single occupants, we were able to automatically detect mild cognitive impairment in older adults with an F 0.5 score of 0.856. Also, we were able to detect the non-amnestic sub-type of mild cognitive impairment in older adults with an F 0.5 score of 0.958.
AB - The public health implications of growing numbers of older adults at risk for dementia places pressure on identifying dementia at its earliest stages so as to develop proactive management plans. The prodromal dementia phase commonly identified as mild cognitive impairment is an important target for this early detection of impending dementia amenable to treatment. In this paper, we propose a method for home-based automatic detection of mild cognitive impairment in older adults through continuous monitoring via unobtrusive sensing technologies. Our method is composed of two main stages: a training stage and a test stage. For training, room activity distributions are estimated for each subject using a time frame of ω weeks, and then affinity propagation is employed to cluster the activity distributions and to extract exemplars to represent the different emerging clusters. For testing, room activity distributions belonging to a test subject with unknown cognitive status are compared to the extracted exemplars and get assigned the labels of the exemplars that result in the smallest normalized Kullbak-Leibler divergence. The labels of the activity distributions are then used to determine the cognitive status of the test subject. Using the sensor and clinical data pertaining to 85 homes with single occupants, we were able to automatically detect mild cognitive impairment in older adults with an F 0.5 score of 0.856. Also, we were able to detect the non-amnestic sub-type of mild cognitive impairment in older adults with an F 0.5 score of 0.958.
KW - Mild cognitive impairment
KW - clustering
KW - generalized linear models
KW - room activity distributions
KW - unobtrusive sensing technologies
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U2 - 10.3233/AIS-160385
DO - 10.3233/AIS-160385
M3 - Article
AN - SCOPUS:84979523445
SN - 1876-1364
VL - 8
SP - 437
EP - 451
JO - Journal of Ambient Intelligence and Smart Environments
JF - Journal of Ambient Intelligence and Smart Environments
IS - 4
ER -