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

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).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.