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Cloud-centric IoT based student healthcare monitoring framework

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Abstract

Among the extensive and impressive collection of applications enabled by IoT, smart and interactive healthcare is a particularly important one. To gather rich information indicator of our mental and physical health, IoT based sensors are either worn on the body or embedded in the living environment. Moreover, by incorporating the mobile computing technology in IoT based healthcare systems, the reactive care system can be transformed to proactive and preventive healthcare systems. Relative to this context, a cloud-centric IoT based smart student m-healthcare monitoring framework is proposed. This framework computes the student diseases severity by predicting the potential disease with its level by temporally mining the health measurements collected from medical and other IoT devices. To effectively analyze the student healthcare data, an architectural model for smart student health care system has been designed. In our case study, health dataset of 182 suspected students are simulated to generate relevant waterborne diseses cases. This data is further analyzed to validate our model by using k-cross validation approach. Pattern based diagnosis scheme is applied using various classification algorithms and then results are computed based on accuracy, sensitivity, specificity and response time. Experimental results show that Decision tree (C4.5) and k-neighest neighbour algorithms perform better as compared to other classifiers in terms of above mentioned parameters. Moreover, the proposed methodology is effective in decision making by delivering time sensitive information to caretaker or doctor within specific time. Lastly, the temporal granule pattern based presentation reterives effective diagnosis results for the proposed system.

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References

  • Amazon (2017) Amazon EC2 instance type. https://aws.amazon.com/ec2. Accessed 16 Apr 2017

  • Arlot S (2010) A survey of cross-validation procedures for model selection. Stat Surv. doi:10.1214/09-SS054

    Article  MathSciNet  MATH  Google Scholar 

  • Atzori L, Iera A, Morabito G (2010) The Internet of Things: a survey. Comput Netw 54(15):2787–2805

    Article  Google Scholar 

  • Baig MM, Gholamhosseini H (2013) Smart health monitoring systems: an overview of design and modeling. J Med Syst 37(2):1–14

    Article  Google Scholar 

  • Banaee H, Ahmed MU, Loutfi A (2013) Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors 13(12):17472–17500

    Article  Google Scholar 

  • Bhatia M, Sood SK (2016) Temporal informative analysis in smart-ICU monitoring: m-healthcare perspective. J Med Syst. doi:10.1007/s10916-016-0547-9

    Article  Google Scholar 

  • Bellos CC, Papadopoulos A, Rosso R, Fotiadis DI (2010) Extraction and analysis of features acquired by wearable sensor networks. IEEE Int Conf Inf Technol Appl Biomed. doi:10.1109/ITAB.2010.5687761

    Article  Google Scholar 

  • Box IM, Yang G, Xie L, Mäntysalo M, Zhou X, Pang Z, Da Xu L, Member S (2014) A health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor and intelligient medicine box. IEEE Trans Ind Inf 10(4):2180–2191

    Article  Google Scholar 

  • Burke HB, Goodman PH, Rosen DB, Henson DE, Weinstein JN, Harrell FE Jr et al (1997) Artificial neural network improve the accuracy of cancer survival prediction. Cancer 79(4):857–862

    Article  Google Scholar 

  • Catarinucci L, De Donno D, Mainetti L, Palano L, Patrono L, Stefanizzi ML, Tarricone L (2015) An IoT-aware architecture for smart healthcare systems. IEEE Internet Things 2(6):515–526

    Article  Google Scholar 

  • Choi J, Ahmed B, Osuna RG (2012) Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Trans Inf Technol Biomed 16(2):279–286

    Article  Google Scholar 

  • Dean J, Ghemawat S (2007) Map reduce. ACM Commun 51(1):217–247

    Google Scholar 

  • Gelogo YE, Hwang HJ, Kim H (2015) Internet of Things (IoT) framework for u-healthcare system. Int J Smart Home 9(11):323–330

    Article  Google Scholar 

  • Gope P, Hwang T (2016) BSN-Care: a secure IoT-based modern healthcare. IEEE Sens J 16(5):1368–1376

    Article  Google Scholar 

  • Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things ( IoT ): a vision, architectural elements, and future directions. Future Gen Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  • Guyon I, Gunn S, Nikravesh M, Zadeh LA (2006) Feature extraction. Springer, New York

    Book  Google Scholar 

  • Hajihashemi Z, Popescu M, Member S (2016) A multidimensional time-series similarity measure with applications to eldercare monitoring. IEEE J Biomed Health Inf 20(3):953–962

    Article  Google Scholar 

  • Hossain MS, Muhammad G (2016) Cloud-assisted industrial Internet of Things (IIoT) enabled framework for health monitoring. Comput Netw 101:192–202

    Article  Google Scholar 

  • Huang F, Wang S, Chan CC (2012) Predicting diseases by using data mining based on healthcare information system. IEEE Conf Granul Comput. doi:10.1109/GrC.2012.6468691

    Article  Google Scholar 

  • Hussain A, Wenbi R, Lopes A, Nadher M, Mudhish M (2015) Health and emergency-care platform for the elderly and disabled people in the Smart City. J Syst Softw 110:253–263

    Article  Google Scholar 

  • Jara J, Zamora-Izquierdo M, Skarmeta F (2013) Interconnection framework for mHealth and remote monitoring based on the Internet of Things. IEEE J Sel Areas Commun 31(9):47–65

    Article  Google Scholar 

  • Kim SH, Chung K (2015) Emergency situation monitoring service using context motion tracking of chronic disease patients. Clust Comput 18(2):747–759

    Article  Google Scholar 

  • La HJ (2016) A conceptual framework for trajectory-based medical analytics with IoT contexts. J Comput Syst Sci 82(4):610–626

    Article  MathSciNet  Google Scholar 

  • Lan Z, Zheng Z, Li Y (2010) Towards automated anomly identification in large-scale systems. IEEE Trans Parellel Distrib Syst 21(2):174–187

    Article  Google Scholar 

  • Lauria EJM, Duchessi PJ (2006) A Bayesian belief network for IT implementation decision support. Decis Support Syst 42(3):1573–1588

    Article  Google Scholar 

  • Lavrac N (1999) Selected techniques for data mining in medicine. Artif Intell Med 16:3–23

    Article  Google Scholar 

  • Liu B, Li J, Chen C, Tan W, Member S, Chen Q, Zhou M (2015) Efficient motif discovery for large-scale time series in healthcare. IEEE Trans Ind Inf 11(3):583–590

    Article  Google Scholar 

  • Liu Y, Xu L, Li M (2016) The parallelization of back propagation neural network in map reduce and spark. Int J Parallel Prog. doi:10.1007/s10766-016-0401-1

    Article  Google Scholar 

  • Lopez-Vallverdu JA, Riano D, Bohada JA (2012) Improving medical decision trees by combining relevant health-care criteria. Expert Syst Appl 39(14):11782–11791

    Article  Google Scholar 

  • Maia P, Batista T, Cavalcante E, Baffa A, Delicato FC, Pires PF, Zomaya A (2014) A web platform for interconnecting body sensors and improving health care. Proced Comput Sci 40:135–142

    Article  Google Scholar 

  • Mao Y, Chen W, Chen Y, Lu C, Kollef M, Bailey T (2012) An integrated data mining approach to real-time clinical monitoring and deterioration warning. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. doi:10.1145/2339530.2339709

  • Melillo P, Orrico A, Scala P, Crispino F, Pecchia L (2015) Cloud-based smart health monitoring system for automatic cardiovascular and fall risk assessment in hypertensive patients. J Med Syst 39(10):1–7

    Article  Google Scholar 

  • Pradhan GN, Chattopadhyay R, Panchanathan S (2010) Processing body sensor data streams for continuous physiological monitoring. Proc Int Conf Multi Media Inf Reterival. doi:10.1145/1743384.1743468

    Article  Google Scholar 

  • Priyanka K, Tripathi NK (2015) A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. Int J Telemed Appl. doi:10.1155/2015/373474

    Article  Google Scholar 

  • Podgoreke V, Kokol P, Stiglic B, Rozman I (2002) Decision tree: an overview and their use in medicine. J Med Syst 26(5):445–463

    Article  Google Scholar 

  • Richards G, Rayward-Smith VJ, Sonksen PH, Corey S, Weng C (2001) Data mining for indicators of early mortality in a database of clinical records. Artif Intell Med 22(3):215–231

    Article  Google Scholar 

  • Scikit-learn (2017). http://scikit.learn.org. Accessed on 13 Apr 2017

  • Wang L, Tao J, Ranjan R, Marten H, Streit A, Chen J, Chen D (2013) G-Hadoop: map-reduce across distributed data centers for data-intensive computing. Future Gen Comput Syst 29(3):739–750

    Article  Google Scholar 

  • Weka 3.6 (2017) toolkit. http://www.cs.waikato.ac.nz/ml/weka. Accessed 18 Apr 2017

  • Xu B, Da Xu L, Member S, Cai H, Xie C, Hu J, Bu F (2014) Ubiquitous data accessing method in IoT-based information system for emergency medical services. IEEE Trans Ind Inf 10(2):1578–1586

    Article  Google Scholar 

  • Xu B, Xu L, Cai H, Jiang L, Luo Y (2015) The design of an m-Health monitoring system based on a cloud computing platform. Enterp Inf Syst. doi:10.1080/17517575.2015.1053416

    Article  Google Scholar 

  • Yeh JY, Wu TH, Tsao CW (2011) Using data mining techniques to predict hospitalization of hemodialysis patients. Decis Support Syst 50(2):439–448

    Article  Google Scholar 

  • Zhu Y (2011) Automatic detection of anomalies in blood glucose using machine learning approach. J Commun Netw 13(2):125–131

    Article  Google Scholar 

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Correspondence to Prabal Verma.

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Verma, P., Sood, S.K. & Kalra, S. Cloud-centric IoT based student healthcare monitoring framework. J Ambient Intell Human Comput 9, 1293–1309 (2018). https://doi.org/10.1007/s12652-017-0520-6

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  • DOI: https://doi.org/10.1007/s12652-017-0520-6

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