Frailty Detection Of Older Adults By Monitoring Their Daily Routine

Different approaches have been proposed in the literature to detect the frailty of an elderly person. In this paper, we propose a solution for detecting the frailty of older adults based on the monitoring of activities of daily living (ADL). The elderly's daily routine, is characterized by indexes determined by depth sensors such as the percentage of time in the lying position, the percentage of time in a sitting position during the day, the number of falls, the number of visits, the number of outing, and the walking speed. These indexes are intended to be an indication of frailty. Measuring frailty is difficult and requires data collection over several months. In this communication, we hypothesize that the elderly person organizes the daily life around their environment, behavior or social relations and has a well-defined routine life and we use a model to simulate the routine (normal) or non-routine (abnormal) day, according to the variance of frailty indexes over a six-month period. The classification of the type of the days (normal/abnormal) for two different databases to lead to an accuracy of 99% and 100%. A patient is considered frail when the weekly percentage of maintaining routine decreases steadily.