Rule-based systems to automatically count bites from meal videos.

Eating behavior is a key factor for nutritional intake and plays a significant role in the development of eating disorders and obesity. The standard methods to detect eating behavior events (i.e., bites and chews) from video recordings rely on manual annotation, which lacks objective assessment and standardization. Yet, video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we present a rule-based system to count bites automatically from video recordings with 468 3D facial key points. We tested the performance against manual annotation in 164 videos from 15 participants. The system can count bites with 79% accuracy when annotation is available, and 71.4% when annotation is unavailable. The system showed consistent performance across varying food textures. Eating behavior researchers can use this automated and objective system to replace manual bite count annotation, provided the system's error is acceptable for the purpose of their study. Utilizing our approach enables real-time bite counting, thereby promoting interventions for healthy eating behaviors. Future studies in this area should explore rule-based systems and machine learning methods with 3D facial key points to extend the automated analysis to other eating events while providing accuracy, interpretability, generalizability, and low computational requirements.