Skeleton‐based human activity recognition for elderly monitoring systems

Abstract

There is a significantly increasing demand for monitoring systems for elderly people in the health‐care sector. As the aging population increases, patient privacy violations and the cost of elderly assistance have driven the research community toward computer vision and image processing to design and deploy new systems for monitoring the elderly in the authors’ society and turning their living houses into smart environments. By exploiting recent advances and the low cost of three‐dimensional (3D) depth sensors such as Microsoft Kinect, the authors propose a new skeleton‐based approach to describe the spatio‐temporal aspects of a human activity sequence, using the Minkowski and cosine distances between the 3D joints. We trained and validated their approach on the Microsoft MSR 3D Action and MSR Daily Activity 3D datasets using the Extremely Randomised Trees algorithm. The results are very promising, demonstrating that the trained model can be used to build a monitoring system for the elderly using open‐source libraries and a low‐cost depth sensor.