Akihide Shibata
Papers
2
Total Citations
28
H-Index
2
About
Akihide Shibata is a pioneering researcher at the intersection of robotics and food science, whose work centers on the objective, quantitative evaluation of food texture. His major contributions lie in developing robotic systems that simulate human mastication to measure and estimate textural properties—a critical challenge for food engineering and elderly care. Shibata’s most cited work introduces a robotic mastication simulator equipped with artificial teeth and tongue, capable of capturing both mechanical and geometric changes during chewing. This system, combined with convolutional neural networks, enables precise estimation of gel-like food texture, moving beyond subjective human panels to reproducible, sensor-driven analysis. With over 28 citations across his top papers, Shibata’s impact is evident in his novel integration of robotics and deep learning for food sensing. His notable achievement includes creating a sensing framework that not only mimics oral processing but also quantifies the dynamic perception of texture, offering transformative tools for designing palatable foods for individuals with dysphagia or other chewing difficulties.
Research Focus
Key Achievements
Top Papers
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- 2