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IC-SMART: IoTCloud enabled Seamless Monitoring for Alzheimer Diagnosis and Rehabilitation SysTem

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Abstract

Alzheimer disease (AD), a progressive neurodegenerative disease is related with the gradual loss of structure or disturbance of neuronal functions and deterioration in cognitive functions. Timely diagnosis of this disease countenances prompt treatment and headways to patient’s quality of life. This paper proposes IC-SMART, a novel IoTCloud based Seamless Monitoring for Alzheimer Diagnosis and Rehabilitation SysTem (SMART) that leverages semantic modeling, specifically, ontology modeling for structuring and representing pertinent knowledge explicit to AD. Patient’s data collected from the “things” such as sensory devices and the inputs provided by the general practitioners and specialists in the AD realm has been integrated in the knowledge base for construction of a Bayesian Network decision model to ascertain the possibility of patient being diagnosed with AD. Furthermore, IC-SMART offers users with the intelligent capabilities of accomplishing multiple control functions such as patient diagnosis, messaging and communication, real time and historical alarm generation, and navigation based assistance to rehabilitation centers. To verify the feasibility of the proposed IC-SMART, an android based mobile cloud software service has been designed and deployed on Amazon EC2 cloud. The classification accuracy of Alzheimer diagnosis using ontology based Bayesian network model has been validated by obtaining the classification results from well-known classifiers such as Naïve Bayes, J48 decision tree and decision stump. Further, sensitivity analysis has been carried out to verify the robustness of IC-SMART. The evaluation results attained for the prototype implementation prove to be very promising.

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References

  • Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 17(4):2347–2376

    Google Scholar 

  • Alzheimer’s Association (2013) Alzheimer’s disease facts and figures. Technical report, Alzheimer’s & Dementia

  • Amira BR, Faouzi B, Hamid A, Bouaziz M (2014) Computer-assisted diagnosis of Alzheimer’s disease. In: IEEE International conference on image processing, applications and systems

  • Ashton K (2009) Internet of things. RFID J 22:97–114

    Google Scholar 

  • Bach FR, Jordan MI (2006) A probabilistic interpretation of canonical correlation analysis. Technical report 688, Department of Statistics, University of California, Berkeley

  • Bodenreider O, Stevens R (2006) Bio-ontoligies: current trends and future directions. Brief Bioinform 7(3):256–274

    Google Scholar 

  • Botta A, De Donato W, Persico V, Pescape A (2016) Integration of cloud computing and internet of things: a survey. Future Gener Comput Syst 56:684–700

    Google Scholar 

  • Bottino CMC et al (2008) Estimate of dementia prevalence in a community sample from sao Paulo. Dement Geriatr Cogn Disord 26(4):291–299

    Google Scholar 

  • Brier G (1950) Verification of forecasts expressed in terms of probability. J Mon Weather Rev 78(1):1–3

    Google Scholar 

  • Bron EE, Alzheimer’s Disease Neuroimaging Initiative et al (2015) Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CAD Dementia challenge. J Neuroimage 111:562–579

    Google Scholar 

  • Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616

    Google Scholar 

  • Chen P, Verma R (2006) A query-based medical information summarization system using ontology knowledge. In: Proceedings of the 19th IEEE symposium on computer-based medical systems, pp 37–42

  • Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. R Stat Soc Ser B 39:1–38

    MathSciNet  MATH  Google Scholar 

  • Detmer DE, Steen EB, Dick RS (1997) The computer-based patient record: an essential technology for health care. Institute of Medicine, Washington

    Google Scholar 

  • Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13:1587–1611

    Google Scholar 

  • Doukas C, Maglogiannis I (2012) Bringing IoT and cloud computing towards pervasive healthcare. In: International conference on innovative mobile and internet services in ubiquitous computing

  • Gartner Says 6.4 Billion Connected “Things” Will Be in Use in 2016, Up 30 Percent From 2015, Gartner Press Release, Nov, 2015

  • Gartner’s 2014 hype cycle for emerging technologies maps the journey to digital business. Gartner Press Release, August 2014

  • Gonzalez DS et al (2010) Computer-aided diagnosis of Alzheimer’s disease using support vector machines and classification trees. Phys Med Biol 55(10):2807–2817

    Google Scholar 

  • Hanley J (1982) Characteristic (ROC) curve. J Radiol 143:29–36

    Google Scholar 

  • Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664

    MATH  Google Scholar 

  • IBM Healthcare (2017) IBM cloud: a growth engine for healthcare. IBM Healthcare

  • Islam SM, Kabir MDH, Hossain M, Kwak K (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708

    Google Scholar 

  • Jack CR et al (2011) Introduction to revised criteria for the diagnosis of Alzheimer’s disease: national institute on aging and the Alzheimer Association Workgroups. Alzheimers Dement 7(3):257–262

    Google Scholar 

  • Kaur PD, Chana I (2014) Cloud based intelligent system for delivering health care as a service. Comput Methods Programs Biomed 113(1):346–359

    Google Scholar 

  • Kohavi R (1995) The power of decision tables. J Mach Learn ECML-95:174–189

    Google Scholar 

  • Larranaga P, Karshenas H, Bielza C, Santana R (2013) A review on evolutionary algorithms in Bayesian network learning and interface tasks. J Inf Sci 233:109–125

    MATH  Google Scholar 

  • Lauritizen SL (1995) The EM algorithm for graphical association models with missing data. Comput Stat Data Anal 19(2):191–201

    Google Scholar 

  • Linthicum D (2016) The cloud and the internet of things are inseparable. InfoWorld. https://www.infoworld.com/article/3021059/cloud-and-internet-of-things-are-inseparable.html. Accessed 10 Oct 2019

  • Lunardi GM, Machot FA, Shekhovtsov VA, Maran V, Machado GM, Machado A, Mayr HC, de Oliveira JPM (2018) IoT-based human action prediction and support. Internet Things 3–4:52–68

    Google Scholar 

  • MacKay DJC (1996) Bayesian methods for backpropagation networks. In: Domany E, van Hemmen JL, Schulten K (eds) Models of neural networks III: association, generalization, and representation. Springer, New York, pp 211–254

    Google Scholar 

  • Malhotra A et al (2014) ADO: a disease ontology representing the domain knowledge specific to Alzheimer’s disease. Alzheimers Dement 10:238–246

    Google Scholar 

  • McKhann G et al (1984) Clinical diagnosis of Alzheimer’s disease. Neurology 34(7):939–944

    Google Scholar 

  • Miorandi D, Sicari S, De Pellegrini F, Chlamtac I (2012) Internet of things: vision, applications and research challenges. Ad Hoc Netw 10(7):1497–1516

    Google Scholar 

  • Miranda IV, Nazeran H, Martinek R (2018) A semantic interoperability approach to heterogeneous internet of medical things (IoMT) platforms. In: IEEE 20th international conference on e-health networking, applications and services (Healthcom)

  • Moussa Y et al (2017) Mobile health technology in late life mental illness: a focused literature review. Am J Geriatr Psych 25(8):865–872

    Google Scholar 

  • Nakhla Z, Nouira K, Ferchichi A (2018) Prescription adverse drug events system (PrescADE) based on ontology and internet of things. Comput J 62:801–805

    Google Scholar 

  • Neal RM (1995) Bayesian learning for neural networks. Ph.D. dissertation, Graduate Department of Computer Science, University of Toronto

  • Olson D, Delen D (2008) Advanced data mining techniques. Springer, New York

    MATH  Google Scholar 

  • Peng C, Goswami P (2019) Meaningful integration of data from heterogeneous health services and home environment based on ontology. J Sens 19(4):1–19

    Google Scholar 

  • Rawashdeh M, Zamil MGA, Hossain MS, Samarah S, Amin SU, Muhammad G (2018) Reliable service delivery in Tele-health care systems. J Netw Comput Appl 115:86–93

    Google Scholar 

  • Riggelsen C (2006) Learning parameters of Bayesian networks from incomplete data via importance sampling. J Approx Reason 42(1):69–83

    MathSciNet  MATH  Google Scholar 

  • Risso NA et al (2016) A cloud-based mobile system to improve respiratory therapy services at home. J Biomed Inform 63:45–53

    Google Scholar 

  • Rodrigues JPC (2016) Mobile health technologies and applications. e-Health Systems, pp 123–139

  • Sharma P, Kaur PD (2017) Effectiveness of web-based social sensing in health information dissemination—a review. Telemat Inform 34(1):194–219

    Google Scholar 

  • Spiess et al P (2009) SOA-based integration of the internet of things in enterprise services. In: IEEE international conference on web services, pp 968–975

  • Sprites P, Glymour CN, Scheines R (2000) Caustion, prediction and search, vol 81. MIT Press, Cambridge

    Google Scholar 

  • Subramaniyaswamy V, Manogaran G, Logesh R, Vijayakumar V, Chilamkurti N, Malathi D, Senthilselvan N (2019) An ontology-driven personalized food recommendation in IoT-based healthcare system. J Supercomput 8:1–33

    Google Scholar 

  • Swaroop KN, Chandu K, Gorrepotu R, Deb S (2019) A health monitoring system for vital signs using IoT. Internet Things 5:116–129

    Google Scholar 

  • Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol) 58(1):267–288

    MathSciNet  MATH  Google Scholar 

  • Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Los Altos

    MATH  Google Scholar 

  • Zadjabbari B, Wongthongtham P, Hussain FK (2010) Ontology based approach in knowledge sharing measurements. J Univers Comput Sci 16(6):956–982

    Google Scholar 

  • Zhang Y, Dong Z, Phillips P, Wang S, Ji G, Yang J, Yuan TF (2015) Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci 9:66

    Google Scholar 

  • Ziming Y, Yinhong Z, Xudong L, Huilong D (2015) A hybrid intelligent diagnosis approach for quick screening of Alzheimer’s disease based on multiple neuropsychological rating scales. Comput Math Methods Med

  • Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc B 67(Part 2):301–320

    MathSciNet  MATH  Google Scholar 

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  1. Guru Nanak Dev University, Jalandhar, India

    Pankaj Deep Kaur & Pallavi Sharma

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  1. Pankaj Deep Kaur
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  2. Pallavi Sharma
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Correspondence to Pankaj Deep Kaur.

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Kaur, P.D., Sharma, P. IC-SMART: IoTCloud enabled Seamless Monitoring for Alzheimer Diagnosis and Rehabilitation SysTem. J Ambient Intell Human Comput 11, 3387–3403 (2020). https://doi.org/10.1007/s12652-019-01534-5

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