Abstract
The automatic recognition of when and for how long a person is eating a certain food or drinking has applications in telecare, smarthome data monetization, and diet control. Existing food recognition systems either recognize the type of the food, but not when and for how long the person was eating and drinking, or use invasive sensors or privacy-intruding cameras which users are hesitant to install in their homes. In this paper, we propose a non-invasive system, using Artificial Intelligence to process 3D point cloud data collected from a 3D mmWave radar, that can distinguish a person's eating and drinking activities from other daily activities. This is challenging because eating and drinking activities are much more fine-grained than activities that existing systems can detect, such as sitting, running, walking, etc. Performance evaluations show that our proposed system significantly outperforms a representative state-of-the-art activity recognition system, RadHAR, by at least 27% and can reach 96.56% and 96.73% accuracy for two different training/testing split setups.