First Bite/Chew: distinguish typical allergic food by two IMUs

Abstract

Eating or overtaking allergic foods may cause fatal symptoms or even death for people with food allergies. Most current food intake tracking methods are camera-based, on-body sensor-based, microphone based, and self-reported. However, challenges that remain are allergic food detection, social acceptance, lightweight, easy to use, and inexpensive. Our approach leverages the first bite/chew and the corresponding hand movement as an indicator to distinguish typical types of the allergic food. Our initial feasibility study shows that our approach can distinguish six types of food at an accuracy of 89.7% over all four participants’ mixed data. Particularly, our method successfully detected and distinguished typical allergic foods such as burgers (wheat), instant noodles (wheat), peanuts, egg fried rice, and edamame, which can be expected to contribute to not only personal use but also medical usage.