Skip to main content

Eyetracking Nutritional Behaviour and Choices

  • Conference paper
  • First Online:
Computer-Human Interaction Research and Applications (CHIRA 2020)

Abstract

Visual attention on nutritional outcomes are currently under investigation. Across a variety of disciplines, visual analytics currently offers many new applications with easy-to-use tools. In this interdisciplinary pilot study, we are refining a novel visual analytics software for assessing dietary and food choices. The goal is to improve our understanding of nutritional behavior when individuals are hungry or satiated. In addition to developing a software toolchain, two null hypotheses were investigated: 1) there is no difference between visual search patterns on food when subjects were hungry and satiated and 2) there is no difference in visual search patterns between subjects when vegetarian and non-vegetarian. The experimental data suggest that food choices may differ from dietary patterns and are slightly correlated with dish gazing. Using visual analytics of food scene perception our study suggests there is likely variation in food gazing pattern when hungry and when satiated. Our study further suggests that participants’ dish-gazing probably could be generalizable; the participants’ gazes on dishes were predicted by shallow artificial neuronal networks (ANN). The mean squared error of the predicted to the real gaze points were \(11.1 \%\), probably indicating that study participants’ scene perception of food could be dirigible. In conclusion, to understand the complicated relationship between scene perception and nutritional behavioral patterns, this pilot study needs to be scaled up to a full study.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Software source code can be downloaded at https://www.hs-osnabrueck.de/prof-dr-julius-schoening/nba.

References

  1. Berkemeyer, S.: Acid–base balance and weight gain: are there crucial links via protein and organic acids in understanding obesity? Med. Hypotheses 73(3), 347–356 (2009). https://doi.org/10.1016/j.mehy.2008.09.059

    Article  Google Scholar 

  2. Berkemeyer, S.: Starvation versus calorie restriction: our road to food insecurity or health. J. Nutr. Food Sci. 01(S1) (2012). https://doi.org/10.4172/2155-9600.s1.004

  3. Berkemeyer, S., Schöning, J.: Feeling hungry–association of dietary patterns with food choices using scene perception. In: Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications (CHIRA). Scitepress (2020). https://doi.org/10.5220/0010146101880195

  4. Breer, N., Gendig, C., Berkemeyer, S.: The relationship of migration, age, income and dietary patterns with body mass index in a cross-sectional analysis of ebagil-study. In: Eighth EUSPR Conference and Members Meeting (2017)

    Google Scholar 

  5. Deutsche Gesellschaft für Ernährung e. V.: Jahresbericht der deutschen gesellschaft für ernährung e. v. 2020 (2020). https://www.dge.de/fileadmin/public/doc/wueu/DGE-Jahresbericht-2020.pdf. Accessed 23 Aug 2021

  6. Deutsche Gesellschaft für Ernährung e. V.: Vollwertige ernährung (2020). https://www.dge.de/ernaehrungspraxis/vollwertige-ernaehrung/. Accessed 23 Aug 2021

  7. Graham, D.J., Roberto, C.A.: Evaluating the impact of US food and drug administration–proposed nutrition facts label changes on young adults’ visual attention and purchase intentions. Health Edu. Behav. 43(4), 389–398 (2016). https://doi.org/10.1177/1090198116651082

  8. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989). https://doi.org/10.1016/0893-6080(89)90020-8

    Article  MATH  Google Scholar 

  9. Keirns, N.G., Hawkins, M.A.W.: Intuitive eating, objective weight status and physical indicators of health. Obes. Sci. Pract. 5(5), 408–415 (2019). https://doi.org/10.1002/osp4.359

    Article  Google Scholar 

  10. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: 25th International Conference on Neural Information Processing Systems (NIPS). NIPS 2012, Curran Associates Inc. (2012). https://doi.org/10.1145/3065386

  11. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  12. MacCormack, J.K., Lindquist, K.A.: Feeling hangry? when hunger is conceptualized as emotion. Emotion 19(2), 301–319 (2019). https://doi.org/10.1037/emo0000422

    Article  Google Scholar 

  13. Ogden, J., Roy-Stanley, C.: How do children make food choices? using a think-aloud method to explore the role of internal and external factors on eating behaviour. Appetite 147, 104551 (2020). https://doi.org/10.1016/j.appet.2019.104551

    Article  Google Scholar 

  14. Payne, B.K., Hall, D.L., Cameron, C.D., Bishara, A.J.: A process model of affect misattribution. Pers. Soc. Psychol. Bull. 36(10), 1397–1408 (2010). https://doi.org/10.1177/0146167210383440

    Article  Google Scholar 

  15. Rubin, O.: The precarious state of famine research. J. Dev. Stud. 55(8), 1633–1653 (2018). https://doi.org/10.1080/00220388.2018.1493196

    Article  Google Scholar 

  16. Schöning, J., Faion, P., Heidemann, G., Krumnack, U.: Providing video annotations in multimedia containers for visualization and research. In: IEEE Winter Conference on Applications of Computer Vision (WACV). Institute of Electrical and Electronics Engineers (IEEE) (2017). https://doi.org/10.1109/wacv.2017.78

  17. Schöning, J., Gert, A., Açik, A., Kietzmann, T., Heidemann, G., König, P.: Exploratory multimodal data analysis with standard multimedia player – multimedia containers: a feasible solution to make multimodal research data accessible to the broad audience. In: Proceedings of the 12th Joint Conference on Computer Vision, Imagingand Computer Graphics Theory and Applications (VISAPP), pp. 272–279. Scitepress (2017). https://doi.org/10.5220/0006260202720279

  18. Schöning, J., Gundler, C., Heidemann, G., König, P., Krumnack, U.: Visual analytics of gaze data with standard multimedia player. J. Eye Mov. Res. 10(5), 1–14 (2017). https://doi.org/10.16910/jemr.10.5.4

  19. Thomas, J.J., Cook, K.A. (eds.): Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE, Los Alamitos (2005)

    Google Scholar 

  20. Turner, M.M., Skubisz, C., Pandya, S.P., Silverman, M., Austin, L.L.: Predicting visual attention to nutrition information on food products: the influence of motivation and ability. J. Health Commun. 19(9), 1017–1029 (2014). https://doi.org/10.1080/10810730.2013.864726

    Article  Google Scholar 

  21. World Medical Association: World medical association declaration of Helsinki. JAMA 310(20), 2191 (2013). https://doi.org/10.1001/jama.2013.281053

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Julius Schöning .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Schöning, J., Berkemeyer, S. (2022). Eyetracking Nutritional Behaviour and Choices. In: Holzinger, A., Silva, H.P., Helfert, M., Constantine, L. (eds) Computer-Human Interaction Research and Applications. CHIRA 2020. Communications in Computer and Information Science, vol 1609. Springer, Cham. https://doi.org/10.1007/978-3-031-22015-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-22015-9_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22014-2

  • Online ISBN: 978-3-031-22015-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics