Device-Free Non-Privacy Invasive Classification of Elderly Travel Patterns in A Smart House Using PIR Sensors and DCNN

Abstract

Single resident life style is increasing among the elderly due to the issues of elderly care cost and privacy invasion. However, the single life style cannot be maintained if they have dementia. Thus, the early detection of dementia is crucial. Systems with wearable devices or cameras are not preferred choice for the long-term monitoring. Main intention of this paper is to propose deep convolutional neural network (DCNN) classifier for indoor travel patterns of elderly people living alone using open data set collected by device-free non-privacy invasive binary (passive infrared) sensor data. Travel patterns are classified as direct, pacing, lapping, or random according to Martino–Saltzman (MS) model. MS travel pattern is highly related with person’s cognitive state, and thus can be used to detect early stage of dementia. We have utilized an open data set that was presented by Center for Advanced Studies in Adaptive Systems project, Washington State University. The data set was collected by monitoring a cognitively normal elderly person by wireless passive infrared sensors for 21 months. First, 117 320 travel episodes are extracted from the data set and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12 000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing data set. Finally, DCNN performance was compared with seven other classical machine-learning classifiers. The random forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching.