Sequential neural networks for multi-resident activity recognition in ambient sensing smart homes

Abstract

Advances in smart home technology and IoT devices had made us capable of monitoring human activities in a non-intrusive way. This data, in turn, enables us to predict the health status and energy consumption patterns of residents of these smart homes. Machine learning has been an excellent tool for the prediction of human activities from raw sensor data of a single resident. However, Multi Resident activity recognition is still a challenging task, as there is no correlation between sensor values and resident activities. In this paper, we have applied deep learning algorithms on the real world ARAS Multi Resident data set, which consists of data from two houses, each with two residents. We have used different variations of Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and their combination on the data set and kept the labels separate for both residents. We have evaluated the performance of models based on several metrics.