Abstract
A healthy diet and lifestyle are the most effective approaches to prevent disease. Good eating habits are central to a healthy lifestyle. When a person eats too much or too little on a continual basis, the risk of disease will increase. Therefore, developing healthy and balanced eating habits is essential to disease prevention. This paper proposes an ontology-based multi-agents (OMAS), including a personal knowledge agent, a fuzzy inference agent, and a semantic generation agent, for evaluating the health of diets. Using the proposed approach, domain experts can create nutritional facts for common Taiwanese foods. Next, the users are requested to input foods eaten. Finally, the food ontology and personal profile ontology are constructed by domain experts. Fuzzy markup language (FML) is used to describe the knowledge base and rule base of the OMAS. Additionally, web ontology language (OWL) is employed to describe the food ontology and personal profile ontology. Finally, the OMAS semantically analyzes dietary status for users based on the pre-constructed ontology and fuzzy inference results. Using the generated semantic analysis, people can obtain health information about what they eat, which can lead to a healthy lifestyle and healthy diet. Experimental results show that the proposed approach works effectively and diet health status can be provided as a reference to promote healthy living.
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References
Acampora G, Loia V (2005a) Using FML and fuzzy technology in adaptive ambient intelligence environments. Int J Comput Intell Res 1(2):171–182
Acampora G, Loia V (2005b) Fuzzy control interoperability and scalability for adaptive domotic framework. IEEE Trans Ind Inform 1(2):97–111
Caceres C, Fernandez A, Ossowski S, Vasirani M (2006) Agent-based semantic service discovery for healthcare: an organizational approach. IEEE Intell Syst 21(6):11–20
Chan V, Ray P, Parameswaran N (2008) Mobile e-Health monitoring: an agent-based approach. IET Commun 2(2):223–230
Chen RS, Chen DK (2008) Apply ontology and agent technology to construct virtual observatory. Exp Syst Appl 34(3):2019–2028
Debenham J, Sierra C (2008) Merging intelligent agency and the Semantic Web. Knowledge Based Syst Arch 21(3):184–191
Ferber J (1999) Multi-Agent Systems: an introduction to distributed artificial intelligence. Addison-Wesley, New York
Frankenfield DC, Muth ER, Rowe WA (1998) The Harris-Benedict studies of human basal metabolism: history and limitations. J Am Diet Assoc 98(4):439–445
Lee CS, Wang MH (2007) Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition. Exp Syst Appl 33(3):606–619
Lee CS, Wang MH (2008) Ontological fuzzy agent for electrocardiogram application. Exp Syst Appl 35(3):1223–1236
Lee CS, Jian ZW, Huang LK (2005) A fuzzy ontology and its application to news summarization. IEEE Trans Syst Man Cybern B 35(5):859–880
Lee CS, Jiang CC, Hsieh TC (2006) A genetic agent using ontology model for meeting scheduling system. Inf Sci 176(9):131–1155
Lee CS, Wang MH, Acampora G, Loia V, Hsu CY (2009) Ontology-based intelligent fuzzy agent for diabetes application. In: Proceeding of the 2009 IEEE Symposium on Computational Intelligence for Intelligent Agents (IA 2009), Nashville, Tennessee, USA, March 30–April 2, pp 16–22
Moreno A (2006) On the evolution of applying agent technology to healthcare. IEEE Intell Syst 21(6):8–10
Moreno A, Valls A, Isern D, Sanchez D (2006) Applying agent technology to healthcare: the CruSMA experience. IEEE Intell Syst 21(6):63–67
Noy NF, McGuinness DL (2001) Ontology development 101: a guide to creating your first ontology. Stanford knowledge systems laboratory technical report KSL-01-05 and Stanford medical informatics technical report SMI-2001-0880
Orgun B, Vu J (2006) HL7 ontology and mobile agents for interoperability in heterogeneous medical information systems. Comput Biol Med 36(7–8):817–836
Reformat M, Ly C (2009) Ontological approach to development of computing with words based systems. Int J Approx Reason 50(1):72–91
Sanchez FG, GarcÃa RV, Bejar RM, F. Breis JT (2009) An ontology, intelligent agent-based framework for the provision of semantic web services. Exp Syst Appl 36(2):3167–3187
US Food and Drug Administration (2009) How to understand and use the nutrition facts label. http://www.cfsan.fda.gov/~dms/foodlab.html. Accessed 2 July 2009
Wang MH, Lee CS, Hsieh KL, Hsu CY, and Chang CC (2009) Intelligent ontological multi-agent for healthy diet planning. In: Proceeding of the 2009 IEEE International Conference on Fuzzy System (FUZZ-IEEE 2009), Jeju Island, Korea, Aug. 19–24, pp 751–756
Zunino A, Campo M (2009) Chronos: a multi-agent system for distributed automatic meeting scheduling. Exp Syst Appl 36(3):7011–7018
Acknowledgments
This work is supported by the National Science Council (NSC) of Taiwan under the grant NSC97-2221-E-024-011-MY2 and NSC98-2221-E-024-009-MY3. The authors would like to thank all subjects for their involving this research project. Ted Knoy is appreciated for his editorial assistance.
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Wang, MH., Lee, CS., Hsieh, KL. et al. Ontology-based multi-agents for intelligent healthcare applications. J Ambient Intell Human Comput 1, 111–131 (2010). https://doi.org/10.1007/s12652-010-0011-5
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DOI: https://doi.org/10.1007/s12652-010-0011-5