2024-03-29T05:08:27Zhttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_oaipmhoai:ipsj.ixsq.nii.ac.jp:001128142023-04-27T10:00:04Z01164:06390:07767:07849
Food Recognition via Monitoring Power Leakage from Microwave OvenFood Recognition via Monitoring Power Leakage from Microwave Ovenenghttp://id.nii.ac.jp/1001/00112789/Technical Reporthttps://ipsj.ixsq.nii.ac.jp/ej/?action=repository_action_common_download&item_id=112814&item_no=1&attribute_id=1&file_no=1Copyright (c) 2015 by the Information Processing Society of JapanGraduate School of Information Science and Technology, The University of TokyoGraduate School of Information Science and Technology, The University of TokyoGraduate School of Information Science and Technology, The University of TokyoGraduate School of Information Science and Technology, The University of TokyoWei, WeiAkihiro, NakamataYoshihiro, KawaharaTohru, AsamiIn 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.AA12628327研究報告コンシューマ・デバイス&システム(CDS)2015-CDS-123192015-01-192015-01-15