Towards a real time kinect signature based human activity assessment at home

Abstract

Tracking Human activity at home plays a growing factor in fields of security, and of bio-medicine. Microsoft Kinect is a non-wearable sensor that aggregate depth images with traditional optical video frames to estimate individuals' joints' location for kinematic analysis. When the subject of interest is out of Kinect coverage, or not in line of sight, the joints' estimations are distorted, which reduce the estimation accuracy, and can lead, in a scenario of multiple subjects, to erroneous estimations' assignment. In this work we derive features from Kinect joints and form a Kinect Signature (KS). This signature is used to identify different patients, differentiate them from others, exclude artifacts and derive the tracking quality. The suggested technology has the potential to assess human kinematics at home, reduce the cost of the patient traveling to the hospital, and improve the medical treatment follow-up.