To contribute to the actualization of the care worker assistance robot, this paper proposes a method for detecting whether the care receiver is chewing from the video sequence acquired by the camera that observes that receiver. The proposed method detects the receiver's face and areas for both cheeks and chin. After applying some normalization to the areas, chewing detection that uses a variable-intensity template is performed, where the template consists of shape models, interest points and intensity distribution model. A likelihood based on the variable-intensity template is computed so that the receiver is judged whether the receiver is chewing. Experiments using seven subjects are conducted. As a result, the accuracy of chewing detection by the proposed method is 83%, which is quite promising.
Atsuto Fujimoto, Takaaki Ohkawauchi, Junji Yamato, Jun Ohya, "An Image Processing Based Method for Chewing Detection Using Variable-intensity Template" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision, 2018, pp 237-1 - 237-6, https://doi.org/10.2352/ISSN.2470-1173.2018.09.IRIACV-237