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Tracking a Person’s Behaviour in a Smart House

  • Gavin Chand
  • Mustafa Ali
  • Bashar BarmadaEmail author
  • Veronica Liesaputra
  • Guillermo Ramirez-Prado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11434)

Abstract

This paper proposes to use machine learning techniques with ultrasonic sensors to predict the behavior and status of a person when they live solely inside their house. The proposed system is tested on a single room. A grid of ultrasonic sensors is placed in the ceiling of a room to monitor the position and the status of a person (standing, sitting, lying down). The sensors readings are wirelessly communicated through a microcontroller to a cloud. An intelligent system will read the sensors values from the cloud and analyses them using machine learning algorithms to predict the person behavior and status and decide whether it is a normal situation or abnormal. If an abnormal situation is concluded, then an alert with be risen on a dashboard, where a care giver can take an immediate action. The proposed system managed to give results with accuracy exceeding 90%. Results out of this project will help people with supported needed, for example elderly people, to live their life as independent as possible, without too much interference from the caregivers. This will also free the care givers and allows them to monitors more units at the same time.

Keywords

Smart home People with supported needs Behavior tracking Ultrasonic sensors Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gavin Chand
    • 1
  • Mustafa Ali
    • 1
  • Bashar Barmada
    • 1
    Email author
  • Veronica Liesaputra
    • 1
  • Guillermo Ramirez-Prado
    • 1
  1. 1.Computer Science DepartmentUnitec Institute of TechnologyAucklandNew Zealand

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