Food Recognition via Monitoring Power Leakage from a Microwave Oven

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Abstract

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 in a microwave oven. By analyzing 180 features that contain the information of heating-time difference, we attain an 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.\n------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.23(2015) No.6(online)------------------------------

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 in a microwave oven. By analyzing 180 features that contain the information of heating-time difference, we attain an 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.\n------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.23(2015) No.6(online)------------------------------

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