Eating detection and chews counting through sensing mastication muscle contraction
Smart Health(2018)
摘要
Unhealthy dietary habits (eating disorder, eating too fast, excessive energy intake, etc.) are major causes of some chronic diseases such as obesity, digestive system disease and diabetes. Dietary monitoring is necessary and important for patients to break and change their unhealthy diet and eating habits. Existing audio or video based methods are often invasive and bring privacy concerns. Motion sensor based related works are popular for eating detection, but cannot count chews. This paper presents the first effort in using motion sensor to sense mastication muscle contraction for continuous dietary monitoring. We observe that during eating the mastication muscles contract and hence bulge in some degree. In addition, the bulge of the mastication muscles has the same frequency as chewing. These observations motivate us to detect eating activity and count chews through attaching a triaxial accelerometer on the temporalis. The proposed method does not record any personal privacy information (audio, video, etc.). The accelerometer is embedded into a headband. Therefore, it is comparatively noninvasive for the user׳s daily living. Experiments are conducted and the results are promising. For eating activity detection, the average accuracy and F-score of five classifiers are 94.4% and 87.2%, respectively, in 10-fold cross validation test using only 5 s of acceleration data. For chews counting, the average error rate of four users is 12.2%.
更多查看译文
关键词
Dietary monitoring,Eating detection,Chews counting,Mastication muscle,Accelerometer
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络