Top

Published in:

2019 | OriginalPaper | Chapter

Recommendation Framework for Diet and Exercise Based on Clinical Data: A Systematic Review

Authors : Vaishali S. Vairale, Samiksha Shukla

Published in: Data Science and Big Data Analytics

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Nowadays, diet and exercise recommender frameworks have gaining expanding consideration because of their importance for living healthy lifestyle. Due of the expanded utilization of the web, people obtain the applicable wellbeing data with respect to their medicinal problem and available medications. Since diseases have a strong relationship with food and exercise, it is especially essential for the patients to focus on adopting good food habits and normal exercise routine. Most existing systems on the diet concentrate on proposals that recommend legitimate food items by considering their food choices or medical issues. These frameworks provide functionalities to monitor nutritional requirement and additionally suggest the clients to change their eating conduct in an interactive way. We present a review of diet and physical activity recommendation frameworks for people suffering from specific diseases in this paper. We demonstrate the advancement made towards recommendation frameworks helping clients to find customized, complex medical facilities or make them available some preventive services measures. We recognize few challenges for diet and exercise recommendation frameworks which are required to be addressed in sensitive areas like health care.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Missing Value Imputation in Medical Records for Remote Health Care

next chapter Security Assessment of SAODV Protocols in Mobile Ad hoc Networks

Phanich M, Pholkul P, Phimoltares S (2010) Food recommendation system using clustering analysis for diabetic patients. In: International conference on information science and applications

Ge M, Elahi M, Fernaández-Tobías I, Ricci F, Massimo D (2015) Using tags and latent factors in a food recommender system. In: Proceedings of the 5th international conference on digital health 2015—DH’15

Runo M (2011) FooDroid: a food recommendation app for university canteens. Swiss Federal Institute of Theology, Zurich

Su CJ, Chen YA, Chih CW (2013) Personalized ubiquitous diet plan service based on ontology and web services. Int J Inf Educ Technol 3(5):522

Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ, Yancy WS (2014) Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care 37(Supplement 1):S120–S143CrossRef

LeFevre ML (2014) Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: US preventive services task force recommendation statement. Ann Intern Med 161(8):587–593CrossRef

Freyne J, Berkovsky S (2013) Evaluating recommender systems for supportive technologies. Hum–Comput Interact Ser 195–217

Svensson M, Laaksolahti J, Höök K, Waern A (2000) A recipe based on-line food store. In: Proceedings of the 5th international conference on intelligent user interfaces IUI’00. ACM, New York, NY, USA, pp 260–263

Elahi M, Ge M, Ricci F, Fern´andez-Tob´ıas I, Berkovsky S, Massimo D (2015) Interaction design in a mobile food recommender system. In: IntRS@recsys, CEUR-WS.org, CEUR workshop proceedings, vol 1438, pp 49–52

10.

Berkovsky S, Freyne J (2010) Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp 111–118

11.

Elsweiler D, Harvey M, Ludwig B, Said A (2015) Bringing the “healthy” into food recommenders. CEUR Workshop Proc 1533:33–36

12.

El-Dosuky MA, Rashad MZ, Hamza TT, El-Bassiouny AH (2012) Food recommendation using ontology and heuristics. AMLTA, Springer, Commun Comput Inf Sci 322:423–429

13.

Aberg J (2006) Dealing with malnutrition: a meal planning system for elderly. AAAI spring symposium: argumentation for consumers of healthcare

14.

Burke R, Felfernig A, Göker M (2011) Recommender systems: an overview. AI Mag. 32:13CrossRef

15.

Ekstrand M (2011) Collaborative filtering recommender systems. Found Trends® Hum-Comput Interact 4:81–173CrossRef

16.

Asanov D (2011) Algorithms and methods in recommender systems. Berlin Institute of Technology, Germany, Berlin

17.

Sarwar B, Karypis G, Konstan J, Reidl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the tenth international conference on World Wide Web—WWW’01

18.

Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42:30–37CrossRef

19.

Bokde D, Girase S, Mukhopadhyay D (2015) Matrix factorization model in collaborative filtering algorithms: a survey. Proced Comput Sci 49:136–146CrossRef

20.

Pazzani MJ, Muramatsu J, Billsus D (1996) Syskill and Webert: identifying interesting web sites. In: Proceedings of the thirteen national conference on artificial intelligent, vol 1, pp 54–61

21.

Burke R (2000) Knowledge-based recommender systems. In: Encyclopedia of library and information systems, vol 69. Marcel Dekker, pp 180–200

22.

Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12(4):331–370CrossRef

23.

Schäfer H, Hors-Fraile S, Karumur R, Calero Valdez A, Said A, Torkamaan H, Ulmer T, Trattner C (2017) Towards health (aware) recommender systems. In: Proceedings of the 2017 international conference on digital health—DH’17

24.

Genitdaridi I, Kondylakis H, Koumakis L, Marias K, Tsiknakis M (2013) Towards intelligent personal health record system: review, criteria and extensions. Proced Comput Sci (Elsevier)

25.

Freyne J, Berkovsky S (2010) Intelligent food planning. In: Proceedings of the 15th international conference on intelligent user interfaces—IUI’10

26.

Forbes P, Zhu M (2011) Content-boosted matrix factorization for recommender systems. In: Proceedings of the fifth ACM conference on recommender systems—RecSys’11

27.

Svensson M, Höök K, Cöster R (2005) Designing and evaluating kalas. ACM Trans Comput-Hum Interact 12:374–400CrossRef

28.

Geleijnse G, Nachtigall P, van Kaam P, Wijgergangs L (2011) A personalized recipe advice system to promote healthful choices. In: Proceedings of the 15th international conference on intelligent user interfaces—IUI’11

29.

Van Pinxteren Y, Geleijnse G, Kamsteeg P (2011) Deriving a recipe similarity measure for recommending healthful meals. In: Proceedings of the 15th international conference on intelligent user interfaces—IUI’11

30.

Ueda M, Asanuma S, Miyawaki Y, Nakajima S (2014) Recipe recommendation method by considering the user’s preference and ingredient quantity of target recipe. In: Proceedings of the international multi conference of engineers and computer scientists, vol 1

31.

Rehman et al (2017) Diet-right: a smart food recommendation system. KSII Trans Internet Inf Syst 11

32.

Al-Nazer A, Helmy T, Al-Mulhem M (2014) User’s profile ontology-based semantic framework for personalized food and nutrition recommendation. Proced Comput Sci 32:101–108CrossRef

33.

Lee C, Wang M-H, Hagras H (2010) A type-2 fuzzy ontology and its application to personal diabetic diet recommendation. IEEE Trans Fuzzy Syst

34.

Kovasznai G (2011) Developing an expert system for diet recommendation. In: 6th IEEE international symposium on applied computational intelligence and informatics (SACI)

35.

Lin E, Yang D, Hung M (2012) System design of an intelligent nutrition consultation and recommendation model. In: 9th international conference on ubiquitous intelligence and computing and 9th international conference on autonomic and trusted computing

36.

Faiz I, Mukhtar H, Qamar A, Khan S (2014) A semantic rules & reasoning based approach for diet and exercise management for diabetics. In: IEEE international conference on emerging technologies (ICET)

37.

Agapito G, Calabrese B, Guzzi P, Cannataro M, Simeoni M, Care I, Lamprinoudi T, Fuiano G, Pujia A (2016) DIETOS: a recommender system for adaptive diet monitoring and personalized food suggestion. In: IEEE 12th international conference on wireless and mobile computing, networking and communications (WiMob)

38.

Kljusurić JG, Kurtanjek Ž (2003) Fuzzy logic modelling in nutrition planning-application on meals in boarding schools. In: Current studies of biotechnology, Vol. III-Food

39.

Kurozumi K et al (2013) FML-based Japanese diet assessment system. In: IEEE international conference on fuzzy systems (FUZZ)

40.

Chen R-C et al (2013) Constructing a diet recommendation system based on fuzzy rules and knapsack method. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer

41.

Mamat M et al (2013) Fuzzy multi-objective linear programming method applied in decision support system to control chronic disease. Appl Math Sci 7(2):61–72MathSciNet

42.

Mamat M et al (2012) Fuzzy linear programming approach in balance diet planning for eating disorder and disease-related lifestyle. Appl Math Sci 6(103):5109–5118MathSciNetMATH

43.

Mák E et al (2010) A formal domain model for dietary and physical activity counseling. In: International conference on knowledge based and intelligent information and engineering systems. Springer

44.

Tao X, Li Y, Zhong N (2011) A personalized ontology model for web information gathering. IEEE Trans Knowl Data Eng 23:496–511CrossRef

45.

Hsiao J, Chang H (2010) SmartDiet: a personal diet consultant for healthy meal planning. In: IEEE 23rd international symposium on computer-based medical systems (CBMS)

46.

Chiang J, Yang P, Tu H (2014) Pattern analysis in daily physical activity data for personal health management. Pervasive Mob Comput 13:13–25CrossRef

47.

Villarreal V, Hervás R, Fdez AD, Bravo J (2009) Applying ontologies in the development of patient mobile monitoring framework. In: 2nd international conference on ehealth and bioengineering—EHB 2009, Romania

48.

Cantais J, Dominguez D, Gigante V, Laera L, Tamma V (2005) An example of food ontology for diabetes control. Working notes of the ISWC 2005 workshop on ontology patterns for the semantic web. Galway, Ireland

49.

Kim J-H, Lee J-H, Park J-S, Lee Y-H, Rim K (2009) Design of diet recommendation system for healthcare service based on user information. In: Fourth international conference on computer sciences and convergence information technology

50.

Khan AS, Hoffmann A (2003) Building a case-based diet recommendation system without a knowledge engineer. Artif Intell Med 27:155–179CrossRef

51.

Izumi S, Kuriyama D, Itabashi G, Togashi A, Kato Y, Takahashi K (2006) An ontology-based advice system for health and exercise. In: Proceedings of the 10th IASTED international conference on internet and multimedia systems and applications 535-029, pp 95–100

52.

Izumi S, Kuriyama D, Miura Y, Yasuda N, Yotsukura R, Kato Y, Takahashi K (2007) Design and implementation of an ontology-based health support system. Technical report of IEICE SS2006-82, pp 19–24

53.

Rokicki M, Herder E, Demidova E (2015) Whats on my plate: towards recommending recipe variations for diabetes patients. In: Proceedings of UMAP15

54.

Freyne J, Berkovsky S (2010) Recommending food: reasoning on recipes and ingredients. In: International conference on user modeling, adaptation, and personalization. Springer, pp 381–386

55.

Achananuparp P, Weber I (2016) Extracting food substitutes from food diary via distributional similarity. arXiv:1607.08807

56.

Ge M, Ricci F, Massimo D (2015) Health-aware food recommender system. In: Proceedings of the 9th ACM conference on recommender systems, pp 333–334

57.

Harvey M, Ludwig B, Elsweiler D (2012) Learning user tastes: a first step to generating healthy meal plans. In: First international workshop on recommendation technologies for lifestyle change

58.

Kieseberg P, Malle B, Fru¨hwirt P, Weippl E, Holzinger A (2016) A tamper-proof audit and control system for the doctor in the loop. Brain Inf 3(4):269–279CrossRef

59.

Kieseberg P, Weippl E, Holzinger A (2016) Trust for the doctor-in-the-loop. In: European research consortium for informatics and mathematics (ERCIM) news: tackling big data in the life sciences, vol 104, issue 1, pp 32–33

60.

Malle B, Kieseberg P, Weippl E, Holzinger A (2016) The right to be forgotten: towards machine learning on perturbed knowledge bases. In: Proceedings of IFIP WG 8.4, 8.9, TC 5 international cross-domain conference on availability, reliability, and security in information systems, CD-ARES 2016 and workshop on privacy aware machine learning for health data science, PAML 2016, Salzburg, Austria, August 31–September 2. Springer, pp 251–266

61.

Rossetti M, Stella F, Zanker M (2016) Contrasting offline and online results when evaluating recommendation algorithms. In: Proceedings of the 10th ACM conference on recommender systems, pp 31–34

62.

Mika S (2011) Challenges for nutrition recommender systems. In: CEUR-WS.org, workshop proceedings on context aware intelligent assistance, pp 25–33

Title: Recommendation Framework for Diet and Exercise Based on Clinical Data: A Systematic Review
Authors: Vaishali S. Vairale
Samiksha Shukla
Publisher: Springer Singapore
Book: Data Science and Big Data Analytics
Print ISBN: 978-981-10-7640-4

Electronic ISBN: 978-981-10-7641-1

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-981-10-7641-1_29

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner