Journal article
Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer's Disease
IEEE journal of biomedical and health informatics, Vol.22(6), pp.1720-1731
11/2018
Handle:
https://hdl.handle.net/2376/107561
PMID: 29994359
Abstract
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). The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavior data to detect the multimodal symptoms that 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 data with corresponding activity classes and extracted time-series 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 symptoms can be predicted from activity-aware smart home data. Similarly, these data can be effectively used to predict reliable changes in mobility and memory skills. Results also suggest that not all behavioral features contribute equally to the prediction of every symptom. Future work therefore can improve model sensitivity by including additional longitudinal data and by further improving strategies to extract 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.
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Details
- Title
- Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer's Disease
- Creators
- Ane Alberdi - Department of Electronics and Computing, Arrasate, SpainAlyssa Weakley - Department of Psychology, Washington State University, Pullman, WA, USAMaureen Schmitter-Edgecombe - Department of Psychology, Washington State University, Pullman, WA, USADiane J Cook - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USAAsier Aztiria - Department of Electronics and Computing, Arrasate, SpainAdrian Basarab - Université de Toulouse, Institut de Recherche en Informatique de Toulouse, Centre National de la Recherche Scientifique, Unité Mixte de Recherche 5505, Université Paul Sabatier, Toulouse, FranceMaitane Barrenechea - Department of Psychology, Washington State University, Pullman, WA, USA
- Publication Details
- IEEE journal of biomedical and health informatics, Vol.22(6), pp.1720-1731
- Academic Unit
- Psychology, Department of; Electrical Engineering and Computer Science, School of
- Publisher
- IEEE
- Grant note
- R01EB015853 / National Institutes of Health (10.13039/100000002)
- Identifiers
- 99900547188301842
- Language
- English
- Resource Type
- Journal article