Machine Learning-Based Unobtrusive Intake Gesture Detection via Wearable Inertial Sensors

Dietary patterns can be the primary reason for many chronic diseases such as diabetes and obesity. State-of-the-art wearable sensor technologies can play a critical role in assisting patients in managing their eating habits by providing meaningful statistics on critical parameters such as the onset, duration, and frequency of eating. For an accurate yet fast food intake recognition, this work presents a novel Machine Learning (ML) based framework that shows promising results by leveraging optimized support vector machine (SVM) classifiers. The SVM classifiers are trained on three comprehensive datasets: OREBA, FIC, and CLEMSON. The developed framework outperforms existing algorithms by achieving F1-scores of 92%, 94%, 95%, and 85% on OREBA-SHA, OREBA-DIS, FIC, and CLEMSON datasets, respectively. In order to assess the generalization aspects, the proposed SVM framework is also trained on one of the three databases while being tested on the others and achieves acceptable F1-scores in all cases. The proposed algorithm is well suited for real-time applications since inference is made using a few support vector parameters compared to thousands in peer deep neural networks models.