A Hybrid Framework for a Comprehensive Physical Activity and Diet Recommendation System

  • Syed Imran Ali
  • Muhammad Bilal Amin
  • Seoungae Kim
  • Sungyoung LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10898)


The quantified self-movement has gained a lot of traction, recently. In this regard, research in personalized wellness support systems has increased. Most of the recommender systems focus on either calorie-burn or calorie-in take objectives. The achievement of calorie-burn objective is through physical activity recommendations while diet recommendations geared towards calorie-in take objectives. A very limited research is performed which track and optimize objectives for both calorie-burn and calorie-in-take, simultaneously based on well-known wellness support guidelines. In this regard, we propose a hybrid recommendation framework, which provides recommendations for physical activity as well as diet recommendation in order to support wellness requirements of a user in a comprehensive manner.


Recommender system Self-quantification Wellness support system 



This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information & communications Technology Promotion)” and by the Korea Research Fellowship Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2016H1D3A1938039).


  1. 1.
    Chan, V., Ray, P., Parameswaran, N.: Mobile E-health monitoring: an agent-based approach. Commun. IET 2(2), 223–230 (2008). Scholar
  2. 2.
    Asabere, N.Y.: Towards a viewpoint of context-aware recommender systems (CARS) and services. Int. J. Comput. Sci. Telecommun. 4(1), 10–29 (2013). Scholar
  3. 3.
    Misfit: Fitness Trackers & Wearable Technology – Accessed 6 Mar 2018
  4. 4.
    AliphCom dba Jawbone (2014). Accessed 6 Mar 2018
  5. 5.
    Fitbit (2018). Accessed 6 Mar 2018
  6. 6.
    Verbert, K., Manouselis, N., Ochoa, X.: Context-aware recommender systems for learning: a survey and future challenges. In: IEEE Transactions.
  7. 7.
    Gómez-Sebastià, I., Moreno, J.: Situated agents and humans in social interaction for elderly healthcare: from Coaalas to AVICENA. J. Med. Syst. (2016).
  8. 8.
    Dharia, S., Jain, V., Patel, J., Vora, J., Chawla, S., Eirinaki, M.: PRO-Fit: a personalized fitness assistant framework. In: 28th International Conference on Software Engineering and Knowledge Engineering. SEKE, Redwood City (2016).
  9. 9.
    Donciu, M., Ionita, M., Dascalu, M., Trausan-Matu, S.: The runner–recommender system of workout and nutrition for runners. In: 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pp. 230–238. IEEE (2011)Google Scholar
  10. 10.
    Charles, E., Stanley, D., Agbaeze, E.: Knowledge-based diet and physical exercise advisory system. Int. J. Sci. Res. (IJSR) 14(7), 2319–7064 (2013). ISSN (Online Index Copernicus Value Impact Factor)Google Scholar
  11. 11.
    Faiz, I., Mukhtar, H., Khan, S.: An integrated approach of diet and exercise recommendations for diabetes patients. In: e-Health Networking, Applications (2014).
  12. 12.
    Omar, A., Wahlqvist, M.: Wellness management through Web-based programmes. J. Telemed. Telecare (2005).

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Syed Imran Ali
    • 1
  • Muhammad Bilal Amin
    • 1
  • Seoungae Kim
    • 1
  • Sungyoung Lee
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringKyung Hee UniversitySeoulSouth Korea

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