Characterization of a Multi-User Indoor Positioning System Based on Low Cost Depth Vision (Kinect) for Monitoring Human Activity in a Smart Home

TL;DR

This paper presents a multi-user indoor positioning system using affordable depth cameras (Kinect) for monitoring human activity in smart homes, aiming to enhance health management and early detection of activity changes.

Contribution

It introduces a calibration method, data fusion approach, and a global positioning projection for depth camera-based indoor positioning systems.

Findings

Effective calibration of low-cost depth cameras achieved.

Multi-camera data fusion improves tracking accuracy.

System supports early detection of activity shifts in smart homes.

Abstract

An increasing number of systems use indoor positioning for many scenarios such as asset tracking, health care, games, manufacturing, logistics, shopping, and security. Many technologies are available and the use of depth cameras is becoming more and more attractive as this kind of device becomes affordable and easy to handle. This paper contributes to the effort of creating an indoor positioning system based on low cost depth cameras (Kinect). A method is proposed to optimize the calibration of the depth cameras, to describe the multi-camera data fusion and to specify a global positioning projection to maintain the compatibility with outdoor positioning systems. The monitoring of the people trajectories at home is intended for the early detection of a shift in daily activities which highlights disabilities and loss of autonomy. This system is meant to improve homecare health management at home for a better end of life at a sustainable cost for the community.