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    Tracking a person’s behaviour in a smart house

    Chand, G.; Ali M.; Barmada, Bashar; Liesaputra, Veronica; Prado, G.

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    Chand, G. (2018) final.pdf (1.156Mb)
    Date
    2018-11
    Citation:
    Chand, G., Ali, M., Barmada, B., Liesaputra, V., & Prado, G. (2018). Tracking a Person’s Behaviour in a Smart House. In C. Pahl, J. Yin,. M. Vukovic,. Q. Yu (Ed.), The 16th International Conference on Service Oriented Computing (pp. 1-12).
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/4765
    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 homes, people with supported needs, behaviour tracking, ultrasonic sensors, machine learning, older people, aged care
    ANZSRC Field of Research:
    080101 Adaptive Agents and Intelligent Robotics, 111702 Aged Health Care

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    • Computing Conference Papers [149]

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