Privacy Preserving Human Fall Recognition Using Human Skeleton Data

Abstract

Fall is a critical danger for a person because he/she can loss any recontroled and stable posture and can face to any associated injuries without any caregiver. Therefore, automatic fall recognition systems have been developed to assist the person falling in short time in place. Moreover, these human-fall recognition systems have also strongly impacted from privacy and the issue of privacy is a challenge and an important societal problem for it. The aim of this paper is to propose an automatic fall recognition from human skeleton joints captured by RGB-Depth Kinect camera supporting a privacy-preservation. The recognition assesses skeleton joints of a person and measures two posture balance (body orientation and centroid height) and two gait motions (velocity and angular acceleration) of this person based on the fall and fall-like representations generated in system’s virtual environment. The system has not de-identified the personal information from original captures of a person and preserves privacy which is highly impacted in fall recognition systems. Experiments on system’s collected human activities using Support Vector Machine learning classification, will support to detect falls in unseen real environments with moderately high recognition accuracy.