Sensor Event Prediction using Recurrent Neural Network in Smart Homes for Older Adults

Abstract

We present preliminary results on sensor data prediction in a smart home environment with a limited number of binary sensors. The data has been collected from a real home with one resident over a period of 17 weeks. We apply Recurrent Neural Network with Long Short-Term Memory to a text sequence derived from the sensors’ events to predict the next event in a sequence. We compare our system’s characteristics and results to a baseline method and to similar work in the area. Our implementation achieved a peak accuracy of 69% for a set with 13 sensors in total - motion, magnetic and power sensors - and 75% for five motion sensors.