Front-Door Event Classification Algorithm for Elderly People Living Alone in Smart House Using Wireless Binary Sensors

Abstract

Many elderly persons prefer to stay alone in a single-resident house for seeking an independent life and reducing the cost of health care. However, the independent life cannot be maintained if the resident develops dementia. Thus, an early detection of dementia is essential for the elderly to extend their independent lifetime. Early symptoms of dementia can be noticed in everyday activities such as front-door events. For example, forgetting something when the person leaves the house might be an early symptom of dementia. In this paper, we introduce a novel front-door events [exit, enter, visitor, other, and brief-return-and-exit (BRE)] classification scheme that validated by using open data sets ( $n =14$ ) collected from 14 testbeds by anonymous wireless binary sensors (passive infrared sensors and magnetic sensors). BRE events occur when four consecutive events (exit-enter-exit-enter) happen in certain time intervals ( $t_{1}$ , $t_{2}$ , and $t_{3})$ , and some of them may be the forget events. Each testbed had one older adult (aged 73 and over) during the experimental period ( $\mu = 547.6 \pm 370.4$ days). The algorithm automatically classifies the resident’s front-door events. Experimental results show the events of total exits, daily exits, out-time per exit, as well as the significance of the $\text{t}_{\mathrm {i}}$ parameters for the number of classified BRE events. Since part of the BRE events may be the forget events, the proposed algorithm could be a useful tool for the forget event detection.