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Does the Gel Biter create an illusion of food texture perception due to differences in mastication speed ?

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Abstract

One of the new computational frameworks is physical reservoir computing. Focusing on this method, we have previously developed a soft-matter artificial mouth ”Gel Biter”, which is composed of multiple polymeric materials based on the structure of the human oral cavity. This soft machine can discriminate even subtle differences in food texture with high accuracy. In general, chewing speed differs from person to person. Then, we focus on the result that brittle foods tend to be chewed faster or more finely based on sensory evaluation in some cognitive studies. This study has analyzed the accuracy of the Gel Biter by changing the parameters of its robotic arm and the differences in food texture perceived when the chewing speed is changed. As a result, there is no significant difference in discrimination accuracy for each speed. The cluster analysis shows that the food characteristics are captured and classified. In addition, the estimation results for Fast chewing indicate that the mechanical mouth also generates the illusion that humans perceive different food textures.

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to protecting the privacy of study participants but are available from the corresponding author on reasonable request.

References

  1. Nakajima K (2019) Range of physical reservoir calculations - using soft robots as an example. Trans Inst Syst, Control Inf Eng 63(12):505–511 (in Japanese)

    Google Scholar 

  2. Tanaka G, Nakane R, Hirose A (2021) Reservoir computing - theory and hardware of fast machine learning for time series pattern recognition. Morikita Publishing Co (in Japanese)

    Google Scholar 

  3. Tanaka G (2019) Concept of reservoir computing and its recent trends. J Inst Electron, Inf Commun Eng 102(2):108–113 (in Japanese)

    Google Scholar 

  4. Hirose K, Sudo I, Ogawa J, Watanabe Y, Shiblee MDNI, Khosla A, Kawakami M, Furukawa H (2022) Gel Biter: food texture discriminator based on physical reservoir computing with multiple soft materials. AROB J 27(4):674–683

  5. Michiwaki Y, Kinumatsu Y, Yokoyama M, Michi K-I, Sumi Y, Ogoshi H, Takahashi T (2001) Difference of conditions of measuring food texture from masticatory movement. Jpn J Dysphagia Rehabil 5(1):20–24 (in Japanese)

    Google Scholar 

  6. Nagasawa Tooru, Okane Hideaki, Sasaki Hajime (1979) Effect of chewing rate on the masticatory function. Jpn J Oral Biol 21:1–8 (in Japanese)

    Article  Google Scholar 

  7. Ohkita Sachiko, Hanasaki Noriko, Wada Yoshiko, Kuragano Taeko (2015) Masticatory muscle activities and textural evaluations of basic biscuits and biscuits prepared with palatinose. J Cook Sci Japan 48(2):95–102 (in Japanese)

    Google Scholar 

  8. Yamaguchi H, Kobayashi R, Takashima Y, Hashidzume A, Harada A (2011) Self-assembly of Gels through molecular recognition of cyclodextrins shape selectivity for linear and cyclic guest molecules. Macromolecules 44(8):2395–2399

    Article  Google Scholar 

  9. Bindra K, Mishra A (2017) A detailed study of clustering algorithms. In: 2017 6th international conference on reliability. Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)

  10. Mclnnes L, Healy J, Melville J (2020) UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 [stat.ML]

  11. Li Youguo, Haiyan Wu (2012) A clustering method based on K-means algorithm. Phys Procedia 25:1104–1109

    Article  Google Scholar 

  12. Palacio-Nino J-O, Berzal F (2019) Evaluation metrics for unsupervised learning algorithms, arXiv:1905.05667v2 [cs.LG]

Download references

Acknowledgements

This work was supported in part by JSPS KAKENHI Grant Number JP17H01224, JP18H05471, JP19H01122, JST COI Grant Number JPMJCE1314, JST-OPERA Program Grant Number JPMJOP1844, JST-OPERA Program Grant Number JPMJOP1614, Moonshot Agriculture, Forestry and Fisheries Research and Development Program (MS508, Grant Number JPJ009237) and the Cabinet Office (CAO), Cross-ministerial Strategic Innovation Promotion Program (SIP), an intelligent knowledge processing infrastructure, integrating physical and virtual domains, and Intensive Support for Young Promising Researchers, Development of Animaloid Technology with Innovative Sensory Communication Capabilities Elicited by Comprehensive Integration of Advanced Soft Matter Material Technologies (funding agency: NEDO).

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Correspondence to Kosuke Hirose.

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This work was presented in part at the joint symposium of the 28th International Symposium on Artificial Life and Robotics, the 8th International Symposium on BioComplexity, and the 6th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Beppu, Oita and Online, January 25–27, 2023).

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Hirose, K., Ogawa, J., Watanabe, Y. et al. Does the Gel Biter create an illusion of food texture perception due to differences in mastication speed ?. Artif Life Robotics 28, 734–740 (2023). https://doi.org/10.1007/s10015-023-00891-x

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  • DOI: https://doi.org/10.1007/s10015-023-00891-x

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