First Bite/Chew: distinguish different types of food by first biting/chewing and the corresponding hand movement

Imbalanced food intake contributes to various diseases, such as obesity, diabetes, high blood pressure, high cholesterol, heart disease, and type-2 diabetes. At the same time, food intake monitoring systems play a significant role in the treatment. Most current food intake tracking methods are camera-based, on-body sensor-based, microphone based, and self-reported. The challenges that remain are social acceptance, lightweight, easy to use, and inexpensive. Our method leverages two 6-axe Inertial Measurement Units (IMU) on the glasses’ leg and the wrist to detect the user’s food intake activities using a machine learning capable Micro Controller Unit (MCU). We introduced the concept of the first bite/chew, which is a stable and reliable indicator to distinguish food types. Our implementation results show that our method can distinguish seven kinds of food at an accuracy of 93.26% (average) over all four participants.