A Social Robot-based Platform towards Automated Diet Tracking

• Autorzy: Alexiadis Anastasios , Triantafyllidis Andreas , Elmas Dimosthenis , Gerovasilis Giorgos , Votis Konstantinos , Tzovaras Dimitrios

Identyfikatory

DOI

10.15439/2020F146

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

deep learning; obesity; image recognition; computational modeling; micro-service architecture; manual; internet

PL

głęboka nauka; rozpoznawanie obrazu; modelowanie obliczeniowe; architektura mikrousług; podręcznik; Internet

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 11--14
• Opis fizyczny: Bibliogr. 20 poz., il.

Twórcy

autor

Alexiadis Anastasios

• Centre for Research and Technology Hellas, Information Technologies Institute (CERTH/ITI), 6km Charilaou-Thermi, Thessaloniki, Greece

autor

Triantafyllidis Andreas

• Centre for Research and Technology Hellas, Information Technologies Institute (CERTH/ITI), 6km Charilaou-Thermi, Thessaloniki, Greece

autor

Elmas Dimosthenis

autor

Gerovasilis Giorgos

autor

Votis Konstantinos

autor

Tzovaras Dimitrios

Bibliografia

• 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.

Uwagi

The study was supported by the European Union‘s HORIZON 2020 Programme (2014-2020), under ID no 777082, and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Ensino e Pesquisa (RNP) under OCARIoT

1. Track 1: Artificial Intelligence

2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).