Non-Contact In-Home Activity Recognition System Utilizing Doppler Sensors

In recent years, various approaches for smart home technology have been developed, such as home appliances control, services for energy saving and support of daily life. In order to realize such services, we need a system which is able to accurately recognize various human activities using low-cost devices. To realize such a system, we need to address several problems: the required sensors are too expensive (P1); it is difficult to precisely recognize place-independent activities like reading (P2), and putting on a device causes a burden to people (P3) the information such as images infringe on the privacy of the occupants (P4). In this paper, we propose a method for activity recognition by utilizing a doppler sensor as a motion detection sensor and a machine learning technique to solve the problems above (P1-P4). Specifically, frequency characteristic is obtained from the signals of the doppler sensor and we construct a machine learning model using effective features, which is presented by Anguita, and speed of target calculated from the doppler frequency. In order to examine the usefulness of the proposed method and find out critical issues of realizing activity recognition, we have collected sensor data of 6 kinds of activities(stationary, smartphone operation, PC operation, reading, writing, and eating) performed by 10 participants. For leave-one-session-out cross-validation, the maximum average recognition accuracy was 95.7%, and the average for 10 participants was 81.0%. For leave-one-person-out cross validation, the average recognition accuracy of logistic regression shows maximum accuracy of 42.1%.