Food Recognition via Monitoring Power Leakage from Microwave Oven Food Recognition via Monitoring Power Leakage from Microwave Oven

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著者

    • Wei Wei
    • Graduate School of Information Science and Technology, The University of Tokyo
    • Akihiro Nakamata
    • Graduate School of Information Science and Technology, The University of Tokyo
    • Tohru Asami
    • Graduate School of Information Science and Technology, The University of Tokyo

抄録

In this paper, we demonstrate a food recognition method by monitoring power leakage from a domestic microwave oven. Universal Software Radio Peripheral (USRP) is applied as a low-cost spectrum analyzer to measure the microwave oven leakage as received signal strength indication (RSSI). We aim to recognize 18 categories of food that are commonly cooked with a microwave oven. By analyzing 180 features that contain the information of heating-time difference, we attain the average recognition accuracy of 82.3%. Using 138 features excluding the heating-time difference information, the average recognition accuracy is 56.2%. The recognition accuracy under different conditions is also investigated, for instance, utilizing different microwave ovens, different distances between the microwave oven and the USRP as well as different data down-sampling rates. Finally, a food recognition application is implemented to demonstrate our method.

In this paper, we demonstrate a food recognition method by monitoring power leakage from a domestic microwave oven. Universal Software Radio Peripheral (USRP) is applied as a low-cost spectrum analyzer to measure the microwave oven leakage as received signal strength indication (RSSI). We aim to recognize 18 categories of food that are commonly cooked with a microwave oven. By analyzing 180 features that contain the information of heating-time difference, we attain the average recognition accuracy of 82.3%. Using 138 features excluding the heating-time difference information, the average recognition accuracy is 56.2%. The recognition accuracy under different conditions is also investigated, for instance, utilizing different microwave ovens, different distances between the microwave oven and the USRP as well as different data down-sampling rates. Finally, a food recognition application is implemented to demonstrate our method.

収録刊行物

  • 情報処理学会研究報告. DCC, デジタルコンテンツクリエーション

    情報処理学会研究報告. DCC, デジタルコンテンツクリエーション 2015-DCC-9(3), 1-9, 2015-01-19

    一般社団法人情報処理学会

各種コード

  • NII論文ID(NAID)
    110009868036
  • NII書誌ID(NCID)
    AA12628338
  • 本文言語コード
    ENG
  • データ提供元
    NII-ELS 
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