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Detection of collaborative activity with Kinect depth cameras.

Loic Sevrin, Norbert Noury, Nacer Abouchi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary
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    This study introduces a system using Kinect depth cameras to monitor elderly individuals' collaborative activities at home. Detecting simultaneous presence helps classify tasks, improving health status analysis and social interaction monitoring.

    Area of Science:

    • Gerontology
    • Computer Science
    • Human-Computer Interaction

    Background:

    • Elderly health is linked to activities and social interactions.
    • Long-term home health monitoring requires analyzing collaborative activities.

    Purpose of the Study:

    • To propose a system for detecting simultaneous presence of multiple elderly subjects in a common area.
    • To enable classification of collaborative vs. non-collaborative tasks within a home environment.

    Main Methods:

    • Utilizing Kinect depth cameras for subject detection and localization.
    • Developing an approach to classify tasks based on spatial-temporal data.
    • Simulating a 24-hour day into a 24-minute scenario for validation.

    Main Results:

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    • The system can detect simultaneous presence of multiple subjects.
    • Localization data aids in classifying activities as collaborative or not.
    • Artifact removal is crucial for achieving high specificity and sensitivity.

    Conclusions:

    • The proposed system offers a preliminary approach for monitoring elderly collaborative activities at home.
    • Further refinement, including artifact removal, is necessary for robust performance in real-world applications.