Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot.

Miguel Cazorla,Félix Escalona,Jose Maria Cañas,Francisco Miguel Rivas,Francisco Gomez-Donoso

doi:10.1155/2019/9412384

Abstract

Ambient assisted living (AAL) environments are currently a key focus of interest as an option to assist and monitor disabled and elderly people. These systems can improve their quality of life and personal autonomy by detecting events such as entering potentially dangerous areas, potential fall events, or extended stays in the same place. Nonetheless, there are areas that remain outside the scope of AAL systems due to the placement of cameras. There also exist sources of danger in the scope of the camera that the AAL system cannot detect. These sources of danger are relatively small in size, occluded, or nonstatic. To solve this problem, we propose the inclusion of a robot which maps such uncovered areas looking for new potentially dangerous areas that go unnoticed by the AAL. The robot then sends this information to the AAL system in order to improve its performance. Experimentation in real-life scenarios successfully validates our approach.

Highlights

It is well known that ambient assisted living (AAL) environments will be a key feature of homes, offices, and even commercial facilities in the near future
There are areas that remain outside the scope of Ambient assisted living (AAL) systems due to the placement of cameras. ere exist sources of danger in the scope of the camera that the AAL system cannot detect. ese sources of danger are relatively small in size, occluded, or nonstatic
We propose the inclusion of a robot which maps such uncovered areas looking for new potentially dangerous areas that go unnoticed by the AAL. e robot sends this information to the AAL system in order to improve its performance

Summary

Introduction

It is well known that ambient assisted living (AAL) environments will be a key feature of homes, offices, and even commercial facilities in the near future. There exist common threats like knives, a dog, or a robot vacuum cleaner which, in addition to their small size, are nonstatic, i.e., their position could change over time In these cases, it is not viable to manually set fixed zones of danger. It is worth noting that there will likely exist occluded areas caused by persons or furniture or Computational Intelligence and Neuroscience even the field of view of the camera. In this case, it is impossible to detect any sources of danger.

Related Works

Background subtraction

Experimentation

Findings

Conclusions