research-article

KARE: a hybrid reasoning approach for promoting active lifestyle

Authors:

Muhammad Hameed Siddiqi,

Byeong Ho Kang,

Sungyoung LeeAuthors Info & Claims

IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

Article No.: 72, Pages 1 - 5

https://doi.org/10.1145/2701126.2701156

Published: 08 January 2015 Publication History

Abstract

Healthcare systems provide suitable services in different domains to help people in fitting themselves into their best pattern of life. This study is focused on the development of a hybrid reasoning engine called KARE (knowledge acquisition and reasoning engine) which is the core reasoning module of ATHENA (activity-awareness for human-engaged wellness applications) platform¹, carried out at UCLab² as a project for promoting active lifestyle. This engine recommends food, mental and physical therapy to the ATHENA users that are based on their personal preferences, historical physical, mental and social health information. In KARE, a hybrid approach is used for reasoning which internally combines the predictions of multiple parallel reasoners into a collective decision. Random Forest, Naïve Bayes and IB1 algorithms are used in parallel in each of the reasoner to generate personalized recommendations for the specified service. The predictions of all the individual reasoners are combined using majority voting scheme to enhance the predictive accuracy of the individual reasoner. The proposed hybrid reasoning approach is tested on real world dataset of weight management, collected under the ATHENA project. The accuracy of correct recommendations for food, physical and mental therapies is 98.7%.

References

[1]

The Ottawa Charter for Health Promotion, 1986. In Proceedings of the An International conference on Health Promotion (World Health Organization (WHO), Ottawa, Canada1986).

[2]

Ahmed, M. U., Banaee, H., and Loutfi, A., 2013. Health monitoring for elderly: an application using case-based reasoning and cluster analysis. ISRN Artificial Intelligence 2013.

[3]

Angevaren, M., Aufdemkampe, G., Verhaar, H. J., Aleman, A., and Vanhees, L., 2008. Physical activity and enhanced fitness to improve cognitive function in older people without known cognitive impairment. Cochrane Database Syst Rev 3, 3.

[4]

Bauman, A. E., 2004. Updating the evidence that physical activity is good for health: an epidemiological review 2000-2003. Journal of Science and Medicine in Sport 7, 1, 6--19.

[5]

Bazan, J. G., Nguyen, H. S., Nguyen, S. H., Synak, P., and Wrã³blewski, J., 2000. Rough set algorithms in classification problem. In Rough set methods and applications Springer, 49--88.

Digital Library

[6]

Bazan, J. G. and Szczuka, M., 2005. The rough set exploration system. In Transactions on Rough Sets III Springer, 37--56.

Digital Library

[7]

Cabrera, M. M. and Edye, E. O., 2010. Integration of rule based expert systems and case based reasoning in an acute bacterial meningitis clinical decision support system. International Journal of Computer Science and Information Security 7, 2.

[8]

Christodoulou, E. and Keravnou, E. T., 1998. Metareasoning and meta-level learning in a hybrid knowledge-based architecture. Artificial Intelligence in Medicine 14, 1, 53--81.

[9]

Do, T. M., Loke, S. W., and Liu, F., 2013. Healthy-Life: An Activity Recognition System with Smartphone Using Logic-Based Stream Reasoning. In Mobile and Ubiquitous Systems: Computing, Networking, and Services Springer, 188--199.

[10]

Elvidge, K., 2012. A hybrid reasoning system for care planning in end-of-life cancer care. In 13th International Conference on Information Reuse and Integration IEEE, 730--733.

[11]

Fahim, M., Idris, M., Ali, R., Nugent, C., Kang, B., Huh, E.-N., and Lee, S., 2014. ATHENA: A Personalized Platform to Promote an Active Lifestyle and Wellbeing Based on Physical, Mental and Social Health Primitives. Sensors 14, 5, 9313--9329.

[12]

Friedli, L., 2009. Mental health, resilience and inequalities WHO Regional Office for Europe Copenhagen.

[13]

Grzymala-Busse, J. W. and Hu, M., 2001. A comparison of several approaches to missing attribute values in data mining. In Rough sets and current trends in computing Springer, 378--385.

Digital Library

[14]

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H., 2009. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter 11, 1, 10--18.

Digital Library

[15]

Hanbing, D., Xia, Z., and Jiren, L., 2011. Knowledge reasoning in health cloud. In International Conference on Cloud and Service Computing (CSC), 48--54. DOI= http://dx.doi.org/10.1109/csc.2011.6138551.

Digital Library

[16]

Kumar, K. A., Singh, Y., and Sanyal, S., 2009. Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU. Expert Systems with Applications 36, 1, 65--71.

Digital Library

[17]

Lee, I. M. and Skerrett, P. J., 2001. Physical activity and all-cause mortality: what is the dose-response relation? Medicine and science in sports and exercise 33, 6; SUPP, S459--S471.

[18]

Lin, R.-H. and Chuang, C.-L., 2010. A hybrid diagnosis model for determining the types of the liver disease. Computers in Biology and Medicine 40, 7, 665--670.

Digital Library

[19]

Montani, S., Magni, P., Bellazzi, R., Larizza, C., Roudsari, A. V., and Carson, E. R., 2003. Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients. Artificial Intelligence in Medicine 29, 1, 131--151.

Digital Library

[20]

Ravana, S. D., Rahman, S. A., and Chan, H. Y., 2007. Web-based diet information system with case-based reasoning capabilities. International Journal of Web Information Systems 2, 3/4, 154--163.

[21]

Rea, B. L., Marshak, H. H., Neish, C., and Davis, N., 2004. The role of health promotion in physical therapy in California, New York, and Tennessee. Physical therapy 84, 6, 510--523.

[22]

Reategui, E. B., Campbell, J. A., and Leao, B. F., 1997. Combining a neural network with case-based reasoning in a diagnostic system. Artificial Intelligence in Medicine 9, 1, 5--27.

[23]

Sekyoung, Y., Goeun, L., Seunghun, P., and Weimo, Z., 2011. Development of remote healthcare system for measuring and promoting healthy lifestyle. Expert Syst With Applications 38, 3, 2828--2834. DOI= http://dx.doi.org/10.1016/j.eswa.2010.08.075.

Digital Library

[24]

Sturman, M. T., Morris, M. C., De Leon, C. F. M., Bienias, J. L., Wilson, R. S., and Evans, D. A., 2005. Physical activity, cognitive activity, and cognitive decline in a biracial community population. Archives of Neurology 62, 11, 1750--1754.

[25]

Verhagen, E. and Engbers, L., 2009. The physical therapist's role in physical activity promotion. British journal of sports medicine 43, 2, 99--101.

[26]

Wang, W. M., Cheung, C. F., Lee, W. B., and Kwok, S. K., 2007. Knowledge based treatment planning for adolescent early intervention of mental healthcare: a hybrid case-based reasoning approach. Expert Systems 24, 4, 232--251.

[27]

Yuan, B. and Herbert, J., 2014. Context-aware hybrid reasoning framework for pervasive healthcare. Personal and ubiquitous computing 18, 4, 865--881.

Digital Library

Cited By

Cai YYu FKumar MGladney RMostafa J(2022)Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping ReviewInternational Journal of Environmental Research and Public Health10.3390/ijerph19221511519:22(15115)Online publication date: 16-Nov-2022
https://doi.org/10.3390/ijerph192215115
Kim H(2020)Ambient intelligence: Placement of Kinect sensors in the home of older adults with visual disabilitiesTechnology and Disability10.3233/TAD-20028732:4(271-283)Online publication date: 20-Nov-2020
https://doi.org/10.3233/TAD-200287
Sharma DSingh Aujla GBajaj R(2020)Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health careTransactions on Emerging Telecommunications Technologies10.1002/ett.4159Online publication date: 27-Oct-2020
https://doi.org/10.1002/ett.4159
Show More Cited By

Index Terms

KARE: a hybrid reasoning approach for promoting active lifestyle

Recommendations

Multimodal hybrid reasoning methodology for personalized wellbeing services

A wellness system provides wellbeing recommendations to support experts in promoting a healthier lifestyle and inducing individuals to adopt healthy habits. Adopting physical activity effectively promotes a healthier lifestyle. A physical activity ...
Tractable default reasoning
Extended constraint handling for CP-networks
Intelligent information processing II

CP-networks are an elegant and compact qualitative framework for express preference, in which we can represent and reason about preference rankings given conditional preference statements. However, represent constraints in such framework is one ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IMCOM '15: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication

January 2015

674 pages

ISBN:9781450333771

DOI:10.1145/2701126

Conference Chairs:
Dongsoo S. Kim
Indiana University
,
Sang-wook Kim
Hanyang University, Korea
,
General Chairs:
Suk-Han Lee
Sungkyunkwan Univerisity, Korea
,
Lajos Hanzo
University of Southampton, UK
,
Roslan Ismail
Universiti Kuala Lumpur, Malaysia

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Research Foundation of Korea(NRF)
Industrial Core Technology Development Program(MOTIE, Korea).

Conference

IMCOM '15

Sponsor:

SIGAPP

IMCOM '15: The 9th International Conference on Ubiquitous Information Management and Communication

January 8 - 10, 2015

Bali, Indonesia

Acceptance Rates

Overall Acceptance Rate 213 of 621 submissions, 34%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
140
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Cai YYu FKumar MGladney RMostafa J(2022)Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping ReviewInternational Journal of Environmental Research and Public Health10.3390/ijerph19221511519:22(15115)Online publication date: 16-Nov-2022
https://doi.org/10.3390/ijerph192215115
Kim H(2020)Ambient intelligence: Placement of Kinect sensors in the home of older adults with visual disabilitiesTechnology and Disability10.3233/TAD-20028732:4(271-283)Online publication date: 20-Nov-2020
https://doi.org/10.3233/TAD-200287
Sharma DSingh Aujla GBajaj R(2020)Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health careTransactions on Emerging Telecommunications Technologies10.1002/ett.4159Online publication date: 27-Oct-2020
https://doi.org/10.1002/ett.4159
Ghanvatkar SKankanhalli ARajan V(2018)User Models for Personalized Physical Activity Interventions: A Scoping Review (Preprint)JMIR mHealth and uHealth10.2196/11098Online publication date: 21-May-2018
https://doi.org/10.2196/11098
Ali RAfzal MSadiq MHussain MAli TLee SKhattak A(2018)Knowledge‐based reasoning and recommendation framework for intelligent decision makingExpert Systems10.1111/exsy.1224235:2Online publication date: 7-Feb-2018
https://doi.org/10.1111/exsy.12242
Ali RAfzal MHussain MAli MSiddiqi MLee SHo Kang B(2016)Multimodal hybrid reasoning methodology for personalized wellbeing servicesComputers in Biology and Medicine10.1016/j.compbiomed.2015.11.01369:C(10-28)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.compbiomed.2015.11.013

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents