Demo: Chewpin: a wearable acoustic device for chewing detection
Yang Chen, Zhitong Cui, Ching Chiuan Yen · 2021
Diet intervention has emerged as a promising strategy in obesity prevention and treatment. Existing research predominantly focused on macronutrient intake and food quantity restriction in diet manipulation. Eating habit is difficult to change due to its highly habitual nature; therefore, it is essential to automatically detect eating behavior and provide real-time intervention in unhealthy eating patterns. In this study, we explored the possibility of designing Chewpin, an easy-to-be-implemented and socially acceptable device for capturing eating behavior(i.e., chewing and swallowing) in a controlled environment. We implemented a convolutional neural network (CNN) for data classification. Overall, our system achieved a promising accuracy of eating recognition of 98.23% on the test set. In the future, we will evaluate its usability and feasibility in real-life eating practices and use this system as a technical tool for problematic eating intervention.