Forecasting Trends in an Ambient Assisted Living Environment Using Deep Learning

Abstract

Ambient Assisted Living (AAL) aims at assisting people in their Activities of Daily Living (ADL). We have seen an increased interest in their applicability to the rural seniors who are slowly losing their autonomy due to aging and chronic diseases. By deploying intelligent devices in the environment of an individual performing their ADLs, we can gather data in the form of a time series. One research venue is to seek to use forecasting techniques to discover trends and predict future trends that could be used to analyze the health of these individuals. With the recent advances in computational power new deep learning forecasting algorithms have been developed. In this paper, we compare a univariate one-dimensional CNN model and a LSTM model that performs multi-step forecasting for one week ahead. The novel dataset used comes from a set of activity and health related sensors deployed in a small apartment that uses our previously designed analytics architecture. We compare these to a forecasting baseline strategy. Both deep learning approaches increase the forecasting accuracy significantly.