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Supervised and Unsupervised Transfer Learning for Activity Recognition from Simple In-home Sensors

In this paper, we propose an approach to improve the accuracy of home activity estimation using device-free sensors in the home. This is achieved by transferring existing training data to a new household considering the differences between households. We assumed two scenarios in which we only have training data from other households with labels and we also have training labels for our own household, and proposed the method to compose supervised transfer between labeled data and unsupervised transfer between labeled unlabeled data for each scenario. To evaluate in realistic settings, we developed the system which consists of an application for use on tablet terminals, which continuously collect light and any optional sensor data, and a Web-based server system that stores the sensor data, estimates activities, provides visualization on users’ Web browsers, and enables users to edit the activity labels. Using the system, we gathered subjects from open called households during a period of approximately four months, and obtained approximately 11,745 activity inputs, approximately 7.14GB of sensor data, and power consumption data of 237,280 hours from 35 households. As a result of evaluation, our method outperformed naive methods, both in the first and second scenarios.