An Unsupervised Framework for Detecting Early Signs of Illness in Eldercare

Abstract

Using non-wearable sensors in eldercare monitoring is a promising solution for improving care and reducing healthcare costs. Abnormal sensor patterns produced by certain resident behaviors can be linked to early signs of illness. We propose an unsupervised framework for detecting abnormal sensor patterns based on clustering activity sensor sequences. We use a 30-day normal window to build a baseline model of an elderly resident by clustering the activity sequences from these days. Each cluster represents different daily activities that are performed in most (normal) days and correspond to normal routines. If a new day contains fewer routine activities, we flag it as abnormal and label the day as one with a possible sign of early illness. A preliminary analysis of the method was conducted on data collected in TigerPlace, an eldercare facility that promotes aging-in-place, with information from our electronic health records (EHR). On a pilot sensor dataset from three residents, with a total of 1902 days, we achieved an average abnormal events prediction of 0.75.