article

Automated assessment of cognitive health using smart home technologies

Authors:

Prafulla N. Dawadi,

Maureen Schmitter-Edgecombe,

Carolyn ParseyAuthors Info & Claims

Technology and Health Care, Volume 21, Issue 4

Pages 323 - 343

Published: 01 July 2013 Publication History

Abstract

BACKGROUND: The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments.OBJECTIVE: This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. METHODS: This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. RESULTS: Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. CONCLUSIONS: The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.

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Cited By

Manea VBerrocal ADe Masi AMøller NWac KBayer HLehmann SAshley EHarle RFarrahi KLane N(2019)LDC '19Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers10.1145/3341162.3347758(878-881)Online publication date: 9-Sep-2019
https://dl.acm.org/doi/10.1145/3341162.3347758
Wang YDantcheva ABroutart JRobert PBremond FBilinski P(2018)Comparing Methods for Assessment of Facial Dynamics in Patients with Major Neurocognitive DisordersComputer Vision – ECCV 2018 Workshops10.1007/978-3-030-11024-6_10(144-157)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-11024-6_10
Jurenoks AJoki D(2017)Sensor Network Information Flow Control Method with Static Coordinator within Internet of Things in Smart House EnvironmentProcedia Computer Science10.1016/j.procs.2017.01.150104:C(385-392)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.01.150
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cover image Technology and Health Care

Technology and Health Care Volume 21, Issue 4

July 2013

125 pages

ISSN:0928-7329

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IOS Press

Netherlands

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Published: 01 July 2013

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Manea VBerrocal ADe Masi AMøller NWac KBayer HLehmann SAshley EHarle RFarrahi KLane N(2019)LDC '19Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers10.1145/3341162.3347758(878-881)Online publication date: 9-Sep-2019
https://dl.acm.org/doi/10.1145/3341162.3347758
Wang YDantcheva ABroutart JRobert PBremond FBilinski P(2018)Comparing Methods for Assessment of Facial Dynamics in Patients with Major Neurocognitive DisordersComputer Vision – ECCV 2018 Workshops10.1007/978-3-030-11024-6_10(144-157)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-11024-6_10
Jurenoks AJoki D(2017)Sensor Network Information Flow Control Method with Static Coordinator within Internet of Things in Smart House EnvironmentProcedia Computer Science10.1016/j.procs.2017.01.150104:C(385-392)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.01.150
Dawadi PCook DSchmitter-Edgecombe M(2016)Modeling patterns of activities using activity curvesPervasive and Mobile Computing10.5555/2938785.293889328:C(51-68)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.5555/2938785.2938893
Stavropoulos TMeditskos GBriassouli AKompatsiaris IBoll SAizawa KBriasouli AGurrin CJalali LMeyer J(2016)Multimodal Sensing and Intelligent Fusion for Remote Dementia Care and SupportProceedings of the 2016 ACM Workshop on Multimedia for Personal Health and Health Care10.1145/2985766.2985776(35-39)Online publication date: 16-Oct-2016
https://dl.acm.org/doi/10.1145/2985766.2985776
Janjua ZRiboni DBettini COssowski S(2016)Towards automatic induction of abnormal behavioral patterns for recognizing mild cognitive impairmentProceedings of the 31st Annual ACM Symposium on Applied Computing10.1145/2851613.2851687(143-148)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.1145/2851613.2851687
Riboni DBettini CCivitarese GJanjua ZHelaoui R(2016)SmartFABERArtificial Intelligence in Medicine10.1016/j.artmed.2015.12.00167:C(57-74)Online publication date: 1-Feb-2016
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Cena FLikavec SRapp AMase KLangheinrich MGatica-Perez DGellersen HChoudhury TYatani K(2015)Quantified self and modeling of human cognitionAdjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers10.1145/2800835.2800954(1021-1026)Online publication date: 7-Sep-2015
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Aced López SCorno FDe Russis L(2015)Supporting caregivers in assisted living facilities for persons with disabilitiesUniversal Access in the Information Society10.1007/s10209-014-0400-114:1(133-144)Online publication date: 1-Mar-2015
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