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2021 | OriginalPaper | Buchkapitel

Recognition of Food-Texture Attributes Using an In-Ear Microphone

verfasst von : Vasileios Papapanagiotou, Christos Diou, Janet van den Boer, Monica Mars, Anastasios Delopoulos

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

Food texture is a complex property; various sensory attributes such as perceived crispiness and wetness have been identified as ways to quantify it. Objective and automatic recognition of these attributes has applications in multiple fields, including health sciences and food engineering. In this work we use an in-ear microphone, commonly used for chewing detection, and propose algorithms for recognizing three food-texture attributes, specifically crispiness, wetness (moisture), and chewiness. We use binary SVMs, one for each attribute, and propose two algorithms: one that recognizes each texture attribute at the chew level and one at the chewing-bout level. We evaluate the proposed algorithms using leave-one-subject-out cross-validation on a dataset with 9 subjects. We also evaluate them using leave-one-food-type-out cross-validation, in order to examine the generalization of our approach to new, unknown food types. Our approach performs very well in recognizing crispiness (0.95 weighted accuracy on new subjects and 0.93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness.

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Vorheriges Kapitel Assessing Individual Dietary Intake in Food Sharing Scenarios with Food and Human Pose Detection

Nächstes Kapitel Visual Aware Hierarchy Based Food Recognition

https://splendid-program.eu/.

Aguayo-Mendoza, M.G., Ketel, E.C., van der Linden, E., Forde, C.G., Piqueras-Fiszman, B., Stieger, M.: Oral processing behavior of drinkable, spoonable and chewable foods is primarily determined by rheological and mechanical food properties. Food Qual. Pref. 71, 87–95 (2019). https://doi.org/10.1016/j.foodqual.2018.06.006CrossRef

Amft, O., Kusserow, M., Troster, G.: Bite weight prediction from acoustic recognition of chewing. IEEE Trans. Biomed. Eng. 56(6), 1663–1672 (2009)CrossRef

Amft, O.: A wearable earpad sensor for chewing monitoring. In: SENSORS, 2010 IEEE, pp. 222–227 (2010). https://doi.org/10.1109/ICSENS.2010.5690449

Amft, O., Stäger, M., Lukowicz, P., Tröster, G.: Analysis of chewing sounds for dietary monitoring. In: Beigl, M., Intille, S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 56–72. Springer, Heidelberg (2005). https://doi.org/10.1007/11551201_4CrossRef

van den Boer, J., et al.: The splendid eating detection sensor: development and feasibility study. JMIR Mhealth Uhealth, p. e170. (2018). https://doi.org/10.2196/mhealth.9781CrossRef

van den Boer, J., Werts, M., Siebelink, E., de Graaf, C., Mars, M.: The availability of slow and fast calories in the dutch diet: the current situation and opportunities for interventions. Foods 6(10), 87 (2017). https://doi.org/10.3390/foods6100087CrossRef

Brouwer-Brolsma, E.M., et al.: Dietary intake assessment: from traditional paper-pencil qestionnaires to technology-based tools. In: Athanasiadis, I.N., Frysinger, StP, Schimak, G., Knibbe, W.J. (eds.) ISESS 2020. IAICT, vol. 554, pp. 7–23. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39815-6_2CrossRef

Bull, A.D.: Convergence rates of efficient global optimization algorithms. arXiv e-prints arXiv:1101.3501 (2011)

Campbell, C.L., Wagoner, T.B., Foegeding, E.A.: Designing foods for satiety: the roles of food structure and oral processing in satiation and satiety. Food Structure 13, 1–12 (2017). https://doi.org/10.1016/j.foostr.2016.08.002CrossRef

10.

Christodoulidis, S., Anthimopoulos, M., Mougiakakou, S.: Food recognition for dietary assessment using deep convolutional neural networks. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds.) ICIAP 2015. LNCS, vol. 9281, pp. 458–465. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23222-5_56CrossRef

11.

Cox, D.N., et al.: Sensory and hedonic judgments of common foods by lean consumers and consumers with obesity. Obesity Res. 6, 438–447 (1998). https://doi.org/10.1002/j.1550-8528.1998.tb00376.xCrossRef

12.

Daillant-Spinnler, B., MacFie, H.J.H., Beyts, P.K., Hedderley, D.: Relationships between perceived sensory properties and major preference directions of 12 varieties of apples from the southern hemisphere. Food Qual. Pref. 7(2), 113–126 (1996). https://doi.org/10.1016/0950-3293(95)00043-7CrossRef

13.

Dehais, J., Anthimopoulos, M., Mougiakakou, S.: Food image segmentation for dietary assessment. In: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management. MADiMa ’16, ACM, New York, NY, USA, pp. 23–28 (2016). /DOIurl10.1145/2986035.2986047

14.

Forde, C.G.: From perception to ingestion; the role of sensory properties in energy selection, eating behaviour and food intake. Food Qual. Pref. 66, 171–177 (2018). https://doi.org/10.1016/j.foodqual.2018.01.010CrossRef

15.

Gelbart, M.A., Snoek, J., Adams, R.P.: Bayesian optimization with unknown constraints. arXiv e-prints arXiv:1403.5607 (2014)

16.

Harker, R.F., Gunson, A.F., Jaeger, S.R.: The case for fruit quality: an interpretive review of consumer attitudes, and preferences for apples. Postharvest Biol. Technol. 28(3), 333–347 (2003). https://doi.org/10.1016/S0925-5214(02)00215-6CrossRef

17.

Lasschuijt, M.P., Mars, M., Stieger, M., Miquel-Kergoat, S., De Graaf, C., Smeets, P.A.M.: Comparison of oro-sensory exposure duration and intensity manipulations on satiation. Physiol. Behav. 176, 76–83 (2017). https://doi.org/10.1016/j.physbeh.2017.02.003CrossRef

18.

Lu, Y., Allegra, D., Anthimopoulos, M., Stanco, F., Farinella, G.M., Mougiakakou, S.: A multi-task learning approach for meal assessment. arXiv e-prints arXiv:1806.10343 (2018)

19.

Muñoz, A.M.: Development and application of texture reference scales. J. Sens. Stud. 1(1), 55–83 (1986). https://doi.org/10.1111/j.1745-459X.1986.tb00159.xCrossRef

20.

Papapanagiotou, V.: Modeling and automatically measuring human eating behavior. Ph.D. thesis, Department Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki (2019)

21.

Papapanagiotou, V., Diou, C., Lingchuan, Z., van den Boer, J., Mars, M., Delopoulos, A.: Fractal nature of chewing sounds. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds.) ICIAP 2015. LNCS, vol. 9281, pp. 401–408. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23222-5_49CrossRef

22.

Papapanagiotou, V., Diou, C., Zhou, L., van den Boer, J., Mars, M., Delopoulos, A.: A novel chewing detection system based on PPG, audio and accelerometry. IEEE J. Biomed. Health Inform. 21(3), 607–618 (2017). https://doi.org/10.1109/JBHI.2016.2625271CrossRef

23.

Päßler, S., Wolff, M., Fischer, W.: Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food. Physiol. Meas. 33(6), 1073–1093 (2012). https://doi.org/10.1088/0967-3334/33/6/1073CrossRef

24.

Péneau, S., et al.: Relating consumer evaluation of apple freshness to sensory and physico-chemical measurements. J. Sens. Stud. 22(3), 313–335 (2007). https://doi.org/10.1111/j.1745-459X.2007.00112.xCrossRef

25.

Pereira, L.J., van der Bilt, A.: The influence of oral processing, food perception and social aspects on food consumption: a review. J. Oral Rehabil. 43(8), 630–648 (2016). https://doi.org/10.1111/joor.12395CrossRef

26.

Péneau, S., Hoehn, E., Roth, H.R., Escher, F., Nuessli, J.: Importance and consumer perception of freshness of apples. Food Qual. Pref. 17(1), 9–19 (2006). https://doi.org/10.1016/j.foodqual.2005.05.002CrossRef

27.

Sazonov, E.S., Makeyev, O., Schuckers, S., Lopez-Meyer, P., Melanson, E.L., Neuman, M.R.: Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior. IEEE Trans. Biomed. Eng. 57(3), 626–633 (2010). https://doi.org/10.1109/TBME.2009.2033037CrossRef

28.

Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. arXiv e-prints arXiv:1206.2944 (2012)

29.

StribiŢcaia, E., Evans, C.E.L., Gibbons, C., Blundell, J., Sarkar, A.: Food texture influences on satiety: systematic review and meta-analysis. Sci. Rep. 10(1), 12929 (2020). https://doi.org/10.1038/s41598-020-69504-yCrossRef

30.

Szczesniak, A.S.: Texture is a sensory property. Food Qual. Pref. 13(4), 215–225 (2002). https://doi.org/10.1016/S0950-3293(01)00039-8CrossRef

31.

Zijlstra, N., de Wijk, R., Mars, M., Stafleu, A., de Graaf, C.: Effect of bite size and oral processing time of a semisolid food on satiation. Am. J. Clin. Nutr. 90(2), 269–275 (2009). https://doi.org/10.3945/ajcn.2009.27694CrossRef

Titel: Recognition of Food-Texture Attributes Using an In-Ear Microphone
verfasst von: Vasileios Papapanagiotou
Christos Diou
Janet van den Boer
Monica Mars
Anastasios Delopoulos
Verlag: Springer International Publishing
Buch: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68820-2

Electronic ISBN: 978-3-030-68821-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-68821-9_46

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