An Unsupervised Framework for Detecting Early Signs of Illness in Eldercare Using nonwearable 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 30day 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 aginginplace, 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.

RFcare: devicefree posture recognition for elderly people using a passive RFID tag array Activity recognition is a fundamental research topic for a wide range of important applications such as fall detection for elderly people. Existing techniques mainly rely on wearable sensors, which may not be reliable and practical in realworld situations since people often forget to wear these sensors. For this reason, devicefree activity recognition has gained the popularity in recent years. In this paper, we propose an RFID (radio frequency identification) based, devicefree posture recognition system. More specifically, we analyze Received Signal Strength Indicator (RSSI) signal patterns from an RFID tag array, and systematically examine the impact of tag configuration on system performance. On top of selected optimal subset of tags, we study the challenges on posture recognition. Apart from exploring posture classification, we specially propose to infer posture transitions via Dirichlet Process Gaussian Mixture Model (DPGMM) based Hidden Markov Model (HMM), which effectively captures the nature of uncertainty caused by signal strength varieties during posture transitions. We run a pilot study to evaluate our system with 12 orientationsensitive postures and a series of posture change sequences. We conduct extensive experiments in both lab and reallife home environments. The results demonstrate that our system achieves high accuracy in both environments, which holds the potential to support assisted living of elderly people.