  Unhealthy dietary habits are considered as the primary cause of various
chronic diseases, including obesity and diabetes. The automatic food intake
monitoring system has the potential to improve the quality of life (QoL) of
people with diet-related diseases through dietary assessment. In this work, we
propose a novel contactless radar-based approach for food intake monitoring.
Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is
employed to recognize fine-grained eating and drinking gestures. The
fine-grained eating/drinking gesture contains a series of movements from
raising the hand to the mouth until putting away the hand from the mouth. A 3D
temporal convolutional network with self-attention (3D-TCN-Att) is developed to
detect and segment eating and drinking gestures in meal sessions by processing
the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this
work collects data in continuous meal sessions (more realistic scenarios). We
create a public dataset comprising 70 meal sessions (4,132 eating gestures and
893 drinking gestures) from 70 participants with a total duration of 1,155
minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are
included in this dataset. To validate the performance of the proposed approach,
seven-fold cross-validation method is applied. The 3D-TCN-Att model achieves a
segmental F1-score of 0.896 and 0.868 for eating and drinking gestures,
respectively. The results of the proposed approach indicate the feasibility of
using radar for fine-grained eating and drinking gesture detection and
segmentation in meal sessions.
