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    Kitchen Activity Detection for Healthcare using a low-power Radar-Enabled Sensor Network 
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    • Kitchen Activity Detection for Healthcare using a low-power Radar-Enabled Sensor Network
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    • School of Electronic Engineering and Computer Science
    • Electronic Engineering and Computer Science
    • Kitchen Activity Detection for Healthcare using a low-power Radar-Enabled Sensor Network
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    Kitchen Activity Detection for Healthcare using a low-power Radar-Enabled Sensor Network

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    Accepted version (16.03Mb)
    Publisher
    Institute of Electrical and Electronics Engineers
    ISSN
    0536-1486
    Metadata
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    Abstract
    Human activity detection plays a crucial role in the recognition of activities of daily living (ADLs). In the past ten years, research on activity detection in the home was achieved through the data aggregation from several different sensors (presence sensors, door contacts, appliances tagging, cameras, wearable beacons, mobile phones, etc.). However, the cost of deployment and maintenance of a multitude of sensor devices and the intrusiveness they can infer are quite high. Research on minimal and non-intrusive sensing for recognition of ADLs are vital for the future of remote care. In this paper, we propose a minimal and non-intrusive low-power low-cost radar-based sensing network system that uses an innovative approach for recognizing human activity in the home. We applied our novel approach to the challenging problem of kitchen activity recognition and investigated fifteen different activities. We designed and trained a deep convolutional neural network (DCNN) that classifies different activities based on their distinct micro-Doppler signatures. We achieved an overall classification rate of 92.8% in activity recognition. Most importantly, in nearly real-time, our approach successfully recognized human activities in more than 89% of the time.
    Authors
    BODANESE, EL; LUO, F; POSLAD, S; IEEE ICC 2019
    URI
    https://qmro.qmul.ac.uk/xmlui/handle/123456789/56670
    Collections
    • Electronic Engineering and Computer Science [1832]
    Copyright statements
    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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