Smart Home-Based Prediction of Multidomain Symptoms 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). 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.

Fig. 1:

Overview of the research methods.

Fig. 2:

Extract of an AR activity-labeled sensor event data stream.

Fig. 3:

Floor plan and sensor layout of one of the smart home sites.

Fig. 4:

Chow-Liu tree for the PCA-reduced dataset.

Fig. 5:

Regression results for the absolute test scores using all behavioral features based on 10-fold CV (statistically significant improvement for r (adjusted *p<0.01,**p<0.001) and for MAE (†p<0.01, ††p<0.001) in comparison to the corresponding pairwise random algorithm)). Bars represent r and lines represent MAE.

Fig. 6:

Regression results for the absolute test scores by behavior feature type based on 10-fold CV (statistically significant improvement for r (adjusted *p<0.01,**p<0.001) and for MAE (†p<0.01) in comparison to the corresponding pairwise random algorithm)). Bars represent r and lines represent MAE.