Detection of privacy-sensitive situations for social robots in smart homes

Abstract

The ubiquitous use of cameras in a home environment raises privacy concerns, which is one of the major barriers to the deployment of smart home systems for elderly and disabled care. Social robots are equipped with cameras that can witness embarrassing situations of their owners, such as nakedness during a morning bath. In this paper, our proposed solution is to let the robot detect such privacy-sensitive moments and then turn away to avoid observing the person. We proposed a method based on the Convolutional Neural Networks (CNN) to detect nakedness in a daily living scenario. In this method, two CNNs are used: one detects whether a person is present or not in the current scene, and the other detects privacy-sensitive situations. We implemented the method on the ASCC home service robot developed in our lab. The CNNs were trained with a database composed of more than 2900 pictures collected from the Internet and from our ASCC Smart Home. In our experiments, we considered three human poses: sitting, standing, and lying. If the robot detects a privacy-sensitive situation, it will turn around and verbally announce its intention, which ensures the person that he or she is not being watched. Our experiments validate the proposed privacy preserving method. We also conducted a user study and find that people prefer that the robot takes some actions to avoid directly watching them in privacy-sensitive situations.