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Smart home-based prediction of multi-domainsymptoms related to Alzheimer's Disease

Alberdi, Ane and Weakley, Alyssa and Schmitter-Edgecombe, Maureen and Cook, Diane J. and Aztiria, Asier and Basarab, Adrian and Barrenechea, Maitane Smart home-based prediction of multi-domainsymptoms related to Alzheimer's Disease. (2018) IEEE Journal of Biomedical and Health Informatics, 22 (6). 1720-1731. ISSN 2168-2194

(Document in English)

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Official URL: https://ieeexplore-ieee-org-s/document/8269303


As members of an increasingly aging society, one of our major priorities is to develop tools to detect the earliest stage of age-related disorders such as Alzheimer’s Disease(AD).Thegoalofthispaperistoevaluatethepossibility of using unobtrusively collected activity-aware smart homebehaviordatatodetectthemultimodalsymptomsthat are often found to be impaired in AD. After gathering longitudinal smart home data for 29 older adults over an average duration of >2 years, we automatically labeled the datawithcorrespondingactivityclassesandextractedtimeseries statistics containing ten behavioral features. Mobility, cognition, and mood were evaluated every six months. Using these data, we created regression models to predict symptoms as measured by the tests and a feature selection analysis was performed. Classification models were built to detect reliable absolute changes in the scores predicting symptoms and SmoteBOOST and wRACOG algorithms were used to overcome class imbalance where needed. Results show that all mobility, cognition, and depression symptomscanbepredictedfromactivity-awaresmarthome data. Similarly, these data can be effectively used to predict reliablechangesinmobilityandmemoryskills.Resultsalso suggest that not all behavioral features contribute equally to the prediction of every symptom. Future work therefore can improve model sensitivity by including additional longitudinaldataandbyfurtherimprovingstrategiestoextract relevant features and address class imbalance. The results presented herein contribute toward the development of an early change detection system based on smart home technology.

Item Type:Article
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org
HAL Id:hal-02548018
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Mondragon Unibertsitatea (SPAIN)
Other partners > Washington State University - WSU (USA)
Laboratory name:
Deposited On:03 Apr 2020 12:31

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