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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 21

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

A Social Robot-based Platform towards Automated Diet Tracking

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DOI: http://dx.doi.org/10.15439/2020F146

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 1114 ()

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Abstract. Diet tracking via self-reports or manual taking of meal photos might be difficult, time-consuming, and discouraging, especially for children, which limits the potential of long-term dietary assessment. We present the design and development of a proof of concept of an automated and unobtrusive system for diet tracking integrating: a) a social robot programmed to automatically capture photos of food and motivate children, b) a deep learning model based on Google Inception V3, applied for the use case of image-based fruit recognition, c) a RESTful microservice architecture deployed to deliver the model outcomes to a platform aiming at childhood obesity prevention. We illustrate the feasibility and virtue of this approach, towards the development of the next-generation computer-assisted systems for automated diet tracking.

References

  1. M. Shields, M. S. Tremblay, S. Connor Gorber, and I. Janssen, “Abdominal obesity and cardiovascular disease risk factors within body mass index categories.,” Heal. reports, vol. 23, no. 2, pp. 7–15, Jun. 2012.
  2. I. Vucenik and J. P. Stains, “Obesity and cancer risk: evidence, mechanisms, and recommendations,” Ann. N. Y. Acad. Sci., vol. 1271, no. 1, pp. 37–43, Oct. 2012, http://dx.doi.org/10.1111/j.1749-6632.2012.06750.x.
  3. E. B. Tate et al., “mHealth approaches to child obesity prevention: successes, unique challenges, and next directions.,” Transl. Behav. Med., vol. 3, no. 4, pp. 406–415, Dec. 2013, http://dx.doi.org/10.1007/s13142-013-0222-3.
  4. A. J. Smith, A. Skow, J. Bodurtha, and S. Kinra, “Health Information Technology in Screening and Treatment of Child Obesity: A Systematic Review,” Pediatrics, vol. 131, no. 3, pp. e894–e902, Mar. 2013, http://dx.doi.org/10.1542/peds.2012-2011.
  5. P. W. C. Lau, E. Y. Lau, D. P. Wong, and L. Ransdell, “A Systematic review of information and communication technology-based interventions for promoting physical activity behavior change in children and adolescents,” J. Med. Internet Res., vol. 13, no. 3, 2011, http://dx.doi.org/10.2196/jmir.1533.
  6. E. P. Abril, “Tracking Myself: Assessing the Contribution of Mobile Technologies for Self-Trackers of Weight, Diet, or Exercise,” J. Health Commun., vol. 21, no. 6, pp. 638–646, Jun. 2016, http://dx.doi.org/10.1080/10810730.2016.1153756.
  7. A. G. Arens-Volland, L. Spassova, and T. Bohn, “Promising approaches of computer-supported dietary assessment and management-Current research status and available applications.,” Int. J. Med. Inform., vol. 84, no. 12, pp. 997–1008, Dec. 2015, http://dx.doi.org/10.1016/j.ijmedinf.2015.08.006.
  8. A. H. Andrew, G. Borriello, and J. Fogarty, “Simplifying mobile phone food diaries,” in Proceedings of the 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, PervasiveHealth 2013, 2013, pp. 260–263, http://dx.doi.org/10.4108/icst.pervasivehealth.2013.252101.
  9. S. M. Schembre et al., “Mobile Ecological Momentary Diet Assessment Methods for Behavioral Research: Systematic Review.,” JMIR mHealth uHealth, vol. 6, no. 11, p. e11170, Nov. 2018, http://dx.doi.org/10.2196/11170.
  10. D. Lupton, “‘I Just Want It to Be Done, Done, Done!’ Food Tracking Apps, Affects, and Agential Capacities,” Multimodal Technol. Interact., vol. 2, no. 2, p. 29, May 2018, http://dx.doi.org/10.3390/mti2020029.
  11. T. Prioleau, E. Moore Ii, and M. Ghovanloo, “Unobtrusive and Wearable Systems for Automatic Dietary Monitoring.,” IEEE Trans. Biomed. Eng., vol. 64, no. 9, pp. 2075–2089, Sep. 2017, http://dx.doi.org/10.1109/TBME.2016.2631246.
  12. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 2818-2826, http://dx.doi.org/10.1109/CVPR.2016.308.
  13. Kawano Y., Yanai K. (2015) Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation. In: Agapito L., Bronstein M., Rother C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science, vol 8927. Springer, Cham
  14. A. Triantafyllidis, A. Alexiadis, D. Elmas, K. Votis, D. Tzovaras, A social robot-based platform for prevention of childhood obesity, in: Proc. - 2019 IEEE 19th Int. Conf. Bioinforma. Bioeng. BIBE 2019, Institute of Electrical and Electronics Engineers Inc., 2019: pp. 914–917. http://dx.doi.org/10.1109/BIBE.2019.00171.
  15. A. Myers et al., “Im2Calories: towards an automated mobile vision food diary,” 2015, 10.1109/ICCV.2015.146.
  16. S. Mezgec and B. Koroušić Seljak, “NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment,” Nutrients, vol. 9, no. 7, p. 657, Jun. 2017, http://dx.doi.org/10.3390/nu9070657.
  17. Y. Hswen, V. Murti, A. Vormawor, R. Bhattacharjee, and J. Naslund, “Virtual avatars, gaming, and social media: Designing a mobile health app to help children choose healthier food options,” J. Mob. Technol. Med., vol. 2, no. 2, p. 8, 2013, http://dx.doi.org/10.7309/jmtm.2.2.3.
  18. O. Mubin, C. J. Stevens, S. Shahid, A. Al Mahmud, and J.-J. Dong, “A Review of the Applicability of Robots in Education,” Technol. Educ. Learn., vol. 1, no. 1, pp. 1–7, 2013, http://dx.doi.org/10.2316/Journal.209.2013.1.209-0015.
  19. O. A. Blanson Henkemans et al., “Design and evaluation of a personal robot playing a self-management education game with children with diabetes type 1.” 01-Jan-2017, http://dx.doi.org/10.1016/j.ijhcs.2017.06.001.
  20. A. Triantafyllidis et al., “Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature,” Artif. Intell. Med., vol. 104, p. 101844, Apr. 2020, http://dx.doi.org/10.1016/j.artmed.2020.101844.