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iEat: Human-food interaction with bio-impedance sensing

Mengxi Liu; Yu Zhang; Bo Zhou; Sizhen Bian; Agnes Grünerbl; Paul Lukowicz
In: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing. ACM International Symposium on Wearable Computers (ISWC-2023), October 8 - September 12, Cancun, Mexico, ACM, 2023.


We explore an atypical use of bio-impedance by leveraging the unique temporal impedance patterns caused by the dynamic circuit changes between a pair of electrodes due to the body motions, and interactions with metal utensils and food during dining activities. Specifically, we present iEat, a wearable impedance-sensing device for automatic food intake monitoring without using external devices such as smart utensils. Using only one impedance channel with one electrode on each wrist, iEat detects food intake activities (e.g. cutting, putting food in the mouth with or without utensils, drinking, etc.) and food types from a defined category. At idle, iEat measures the normal body impedance between the wrists; while eating, new parallel circuits will be formed between the hands through the utensils and food. To quantitatively evaluate iEat in real-world settings, a food intake experiment was conducted including 40 meals performed by ten volunteers in a realistic table-dining environment. With a light-weight convolutional neural network and leaving one subject out cross-validation, iEat could detect five food intake-related activities with 86.27 % average accuracy, and classify eight types of foods with 77.73 % average accuracy.


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