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    Activity recognition and resident identification in smart home environment

    Kashyap, Venkatesh Subramanya

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    MComp_2020_Kashyap Venkatesh +.pdf (3.637Mb)
    Date
    2020
    Citation:
    Kashyap, V. S. (2020). Activity recognition and resident identification in smart home environment. (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Computing). Unitec Institute of Technology, Auckland, New Zealand. Retrieved from https://hdl.handle.net/10652/4914
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/4914
    Abstract
    World’s population is ageing rapidly. There have been various efforts to improve the quality of life for elderly. Ambient assisted living is one possible solution which enables elderly or disabled people to live a better lifestyle. Currently there are smart home systems that utilize a wide range of sensors to predict our everyday activities. However, research into activity recognition and resident identification using ultrasonic sensors are limited. This work introduces machine learning techniques with ultrasonic sensors to predict the activities of one and two person in the smart home environment. The proposed system is capable of recognising the activities and identifying the residents without the need to manually label the prior activities. Our evaluation demonstartes that the proposed approach can predict resident’s activities with high accuracy. The trained model could be used to predict other resident’s activities and also identify resident’s from each other. This research enables the smart home system to be widely adopted in people’s houses with minimal training and also enable people who need support, to live independently with less interference from caregivers which in turn enables caregivers to manage more people at the same time.
    Keywords:
    New Zealand, smart homes, people with supported needs, behaviour tracking, ultrasonic sensors, machine learning, older people, aged care, resident identification
    ANZSRC Field of Research:
    080101 Adaptive Agents and Intelligent Robotics, 111702 Aged Health Care
    Degree:
    Master of Computing, Unitec Institute of Technology
    Supervisors:
    Barmada, Bashar; Ramirez-Prado, Guillermo; Liesaputra, Veronica
    Copyright Holder:
    Author

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    All rights reserved
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use. These documents or images may be used for research or private study purposes. Whether they can be used for any other purpose depends upon the Copyright Notice above. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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    • Computing Dissertations and Theses [90]

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