A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare
- 1.7k Downloads
Advances supported by emerging wearable technologies in healthcare promise patients a provision of high quality of care. Wearable computing systems represent one of the most thrust areas used to transform traditional healthcare systems into active systems able to continuously monitor and control the patients’ health in order to manage their care at an early stage. However, their proliferation creates challenges related to data management and integration. The diversity and variety of wearable data related to healthcare, their huge volume and their distribution make data processing and analytics more difficult. In this paper, we propose a generic semantic big data architecture based on the “Knowledge as a Service” approach to cope with heterogeneity and scalability challenges. Our main contribution focuses on enriching the NIST Big Data model with semantics in order to smartly understand the collected data, and generate more accurate and valuable information by correlating scattered medical data stemming from multiple wearable devices or/and from other distributed data sources. We have implemented and evaluated a Wearable KaaS platform to smartly manage heterogeneous data coming from wearable devices in order to assist the physicians in supervising the patient health evolution and keep the patient up-to-date about his/her status.
KeywordsHealthcare Wearable computing Heterogeneity Big data Data integration Scalability
This research is entirely funded by the National Research Fund (FNR) of Luxembourg under the AFR project.
- 3.Research, A., Wearable Sports and Fitness Devices Will Hit 90 Million Shipments in 2017, 2012 https://www.abiresearch.com/press/wearable-sports-and-fitness-devices-will-hit-90-mi. Accessed 11 Nov 2014
- 4.Saponas TS, Lester J, Hartung C, Kohno T (2006) Devices that tell on you: The nike+ ipod sport kit. Dept of Computer Science and Engineering, University of Washington, Tech RepGoogle Scholar
- 8.Mena, L. J., Felix, V. G., Ostos, R., Gonzalez, J. A., Cervantes, A., Ochoa, A., Ruiz, C., Ramos, R., and Maestre, G. E., Mobile personal health system for ambulatory blood pressure monitoring. Comput Math Methods Med 2013:598196, 2013. doi: 10.1155/2013/598196.PubMedCentralCrossRefPubMedGoogle Scholar
- 13.International Data Corporation (IDC). http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm. Accessed 11 November 2014
- 16.Almorsy M, Grundy J, Ibrahim AS Collaboration-based cloud computing security management framework. In: Cloud Computing (CLOUD), 2011 I.E. International Conference on, 2011. IEEE, pp 364-371. doi: 10.1109/CLOUD.2011.23
- 21.Liu F, Tong J, Mao J, Bohn R, Messina J, Badger L, Leaf D (2011) NIST cloud computing reference architecture. NIST special publication 500:292 http://www.nist.gov/customcf/get_pdf.cfm?pub_id=909505, Accessed 11 Nov 2014
- 22.Levin O, Ketner J, Krapohl D (2013) NIST Big Data Public Working Group: Reference Architecture Subgroup. http://jtc1bigdatasg.nist.gov/_workshop/06_NIST_BD_RefArch.pdf. Accessed 11 Nov 2014
- 23.Farran, B., Channanath, A. M., Behbehani, K., and Thanaraj, T. A., Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait—a cohort study. BMJ open 3(5), e002457, 2013. doi: 10.1136/bmjopen-2012-002457.PubMedCentralCrossRefPubMedGoogle Scholar
- 26.Krötzsch M, Vrandečić D, Völkel M., Semantic mediawiki. In: Proceedings of the 5th International Semantic Web Conference (ISWC2006). 4273:935–942, 2006 doi: 10.1007/11926078_68
- 28.Ruiz-Zafra Á, Noguera M, Benghazi K, Towards a Model-Driven Approach for Sensor Management in Wireless Body Area Networks. In: Fortino G, Di Fatta G, Li W, Ochoa S, Cuzzocrea A, Pathan M (eds) Internet and Distributed Computing Systems, vol 8729. Lecture Notes in Computer Science. Springer International Publishing, pp 335-347, 2014 doi: 10.1007/978-3-319-11692-1_29