Adaptive Smart Environments: Detecting Human Behaviour from Multimodal Observation

Rory Heffernan

    Research output: Contribution to conferencePaperpeer-review


    It is desirable to enhance the social capabilities of
    a smart home environment to become more aware of the
    context of the human occupants’ activities. By taking human
    behavioural and contextual information into account, this will
    potentially improve decision making by the various smart
    house systems. Full mesh Wireless Sensor Networks (WSN)
    can be used for passive localisation and tracking of people
    or objects within a smart home. By monitoring changes in
    the propagation field of the monitored area from the link
    quality measurements collected from all the nodes of the
    network, it is feasible to infer target locations. It is planned
    to apply techniques from Radio Tomographic Imaging (RTI)
    and machine vision methods, adapted to the idiosyncrasies of
    RTI, which will facilitate real-time multiple target tracking in
    the University of Hertfordshire Robot House (UHRH). Using
    the Robot Operating System (ROS) framework, these data
    may then be fused with concurrent data acquired from other
    sensor systems (e.g.) 3-D video tracking and ambient audio
    detection in order to develop a high level contextual data model
    for human behaviour in a smart environment. We present
    experimental results which could provide support for human
    activity recognition in smart environments.
    Original languageEnglish
    Number of pages6
    Publication statusPublished - 24 Apr 2016
    Event9th International Conference on Advances in Computer-Human Interactions (ACHI) - Venice, Italy
    Duration: 24 Apr 201628 Apr 2016


    Conference9th International Conference on Advances in Computer-Human Interactions (ACHI)
    Abbreviated titleACHI 2016
    Internet address


    • radio tomography; device-free passive localisation; wireless sensor networks; human-computer interaction; sensor fusion


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