A temporal analysis system for early detection of health changes

Abstract

A Gaussian mixture model (GMM), coupled with possibilistic clustering is used to build an adaptive system for analyzing streaming multi-dimensional activity feature vector with the goal of identifying signs of early diseases. The system is based on temporal analysis, including outlier detection, customization and adaption to new changes, together with the creation of new components for GMM in the case of emerging new normal patterns. On the other hand, an alert will be fired when detecting unexpected behavior patterns. When dealing with streaming data from embedded sensors in an eldercare environment, every resident has their unique behavior pattern. Therefore, number of Gaussians for the GMM needs to be individually determined. For this reason, possibilistic C-Means (PCM) and Automatic Merging possibilistic Clustering Method (AMPCM) are combined together to cluster the initial data points, detect anomalies and initialize the GMM. The system achieves our goals when tested on the synthetic datasets simulating an extended period of time. We hope that by applying the proposed system in real datasets, it will help by detecting health changes before real health issue happens.