SmrtFridge: IoT-based, user interaction-driven food item & quantity sensing

• Main Authors: SHARMA, Amit, MISRA, Archan, SUBRAMANIAM, Vengateswaran, LEE, Youngki
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Object Segmentation; IR sensing; Internet of Things (IoT); Health Information Technology; Software Engineering; Technology and Innovation
• Online Access: https://ink.library.smu.edu.sg/sis_research/4646; https://ink.library.smu.edu.sg/context/sis_research/article/5649/viewcontent/SmrtFridge_pv_oa.pdf
• Institution: Singapore Management University

We present SmrtFridge, a consumer-grade smart fridge prototype that demonstrates two key capabilities: (a) identify the individual food items that users place in or remove from a fridge, and (b) estimate the residual quantity of food items inside a refrigerated container (opaque or transparent). Notably, both of these inferences are performed unobtrusively, without requiring any explicit user action or tagging of food objects. To achieve these capabilities, SmrtFridge uses a novel interaction-driven, multi-modal sensing pipeline, where Infrared (IR) and RGB video sensing, triggered whenever a user interacts naturally with the fridge, is used to extract a foreground visual image of the food item, which is then processed by a state-of-the-art DNN classifier. Concurrently, the residual food quantity is estimated by exploiting slight thermal differences, between the empty and filled portions of the container. Experimental studies, involving 12 users interacting naturally with 19 common food items and a commodity fridge, show that SmrtFridge is able to (a) extract at least 75% of a food item's image in over 97% of interaction episodes, and consequently identify the individual food items with precision/recall values of ~ 85%, and (b) perform robust coarse-grained (3 level) classification of the residual food quantity with an accuracy of ~ 75%.