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

  • Pankaj Deep KaurEmail author
  • Pallavi Sharma
Original Research


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.


Neurological disorder Alzheimer’s disease (AD) diagnosis Ontology Bayesian network (BN) IoTCloud based healthcare 



  1. 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):2347–2376CrossRefGoogle Scholar
  2. Alzheimer’s Association (2013) Alzheimer’s disease facts and figures. Technical report, Alzheimer’s & DementiaGoogle Scholar
  3. 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 systemsGoogle Scholar
  4. Ashton K (2009) Internet of things. RFID J 22:97–114Google Scholar
  5. Bach FR, Jordan MI (2006) A probabilistic interpretation of canonical correlation analysis. Technical report 688, Department of Statistics, University of California, BerkeleyGoogle Scholar
  6. Bodenreider O, Stevens R (2006) Bio-ontoligies: current trends and future directions. Brief Bioinform 7(3):256–274CrossRefGoogle Scholar
  7. Botta A, De Donato W, Persico V, Pescape A (2016) Integration of cloud computing and internet of things: a survey. Future Gener Comput Syst 56:684–700CrossRefGoogle Scholar
  8. Bottino CMC et al (2008) Estimate of dementia prevalence in a community sample from sao Paulo. Dement Geriatr Cogn Disord 26(4):291–299CrossRefGoogle Scholar
  9. Brier G (1950) Verification of forecasts expressed in terms of probability. J Mon Weather Rev 78(1):1–3CrossRefGoogle Scholar
  10. 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 111:562–579CrossRefGoogle Scholar
  11. 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):599–616CrossRefGoogle Scholar
  12. 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 37–42Google Scholar
  13. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. R Stat Soc Ser B 39:1–38MathSciNetzbMATHGoogle Scholar
  14. Detmer DE, Steen EB, Dick RS (1997) The computer-based patient record: an essential technology for health care. Institute of Medicine, WashingtonGoogle Scholar
  15. Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13:1587–1611CrossRefGoogle Scholar
  16. Doukas C, Maglogiannis I (2012) Bringing IoT and cloud computing towards pervasive healthcare. In: International conference on innovative mobile and internet services in ubiquitous computingGoogle Scholar
  17. Gartner Says 6.4 Billion Connected “Things” Will Be in Use in 2016, Up 30 Percent From 2015, Gartner Press Release, Nov, 2015Google Scholar
  18. Gartner’s 2014 hype cycle for emerging technologies maps the journey to digital business. Gartner Press Release, August 2014Google Scholar
  19. Gonzalez DS et al (2010) Computer-aided diagnosis of Alzheimer’s disease using support vector machines and classification trees. Phys Med Biol 55(10):2807–2817CrossRefGoogle Scholar
  20. Hanley J (1982) Characteristic (ROC) curve. J Radiol 143:29–36CrossRefGoogle Scholar
  21. Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664CrossRefGoogle Scholar
  22. IBM Healthcare (2017) IBM cloud: a growth engine for healthcare. IBM HealthcareGoogle Scholar
  23. Islam SM, Kabir MDH, Hossain M, Kwak K (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708CrossRefGoogle Scholar
  24. 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):257–262CrossRefGoogle Scholar
  25. Kaur PD, Chana I (2014) Cloud based intelligent system for delivering health care as a service. Comput Methods Programs Biomed 113(1):346–359CrossRefGoogle Scholar
  26. Kohavi R (1995) The power of decision tables. J Mach Learn ECML-95:174–189Google Scholar
  27. Larranaga P, Karshenas H, Bielza C, Santana R (2013) A review on evolutionary algorithms in Bayesian network learning and interface tasks. J Inf Sci 233:109–125CrossRefGoogle Scholar
  28. Lauritizen SL (1995) The EM algorithm for graphical association models with missing data. Comput Stat Data Anal 19(2):191–201CrossRefGoogle Scholar
  29. Linthicum D (2016) The cloud and the internet of things are inseparable. InfoWorld. Accessed 10 Oct 2019
  30. 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 3–4:52–68CrossRefGoogle Scholar
  31. 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 211–254CrossRefGoogle Scholar
  32. Malhotra A et al (2014) ADO: a disease ontology representing the domain knowledge specific to Alzheimer’s disease. Alzheimers Dement 10:238–246CrossRefGoogle Scholar
  33. McKhann G et al (1984) Clinical diagnosis of Alzheimer’s disease. Neurology 34(7):939–944CrossRefGoogle Scholar
  34. Miorandi D, Sicari S, De Pellegrini F, Chlamtac I (2012) Internet of things: vision, applications and research challenges. Ad Hoc Netw 10(7):1497–1516CrossRefGoogle Scholar
  35. 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)Google Scholar
  36. Moussa Y et al (2017) Mobile health technology in late life mental illness: a focused literature review. Am J Geriatr Psych 25(8):865–872CrossRefGoogle Scholar
  37. Nakhla Z, Nouira K, Ferchichi A (2018) Prescription adverse drug events system (PrescADE) based on ontology and internet of things. Comput J 62:801–805CrossRefGoogle Scholar
  38. Neal RM (1995) Bayesian learning for neural networks. Ph.D. dissertation, Graduate Department of Computer Science, University of TorontoGoogle Scholar
  39. Olson D, Delen D (2008) Advanced data mining techniques. Springer, New YorkzbMATHGoogle Scholar
  40. Peng C, Goswami P (2019) Meaningful integration of data from heterogeneous health services and home environment based on ontology. J Sens 19(4):1–19CrossRefGoogle Scholar
  41. 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 115:86–93CrossRefGoogle Scholar
  42. Riggelsen C (2006) Learning parameters of Bayesian networks from incomplete data via importance sampling. J Approx Reason 42(1):69–83MathSciNetCrossRefGoogle Scholar
  43. Risso NA et al (2016) A cloud-based mobile system to improve respiratory therapy services at home. J Biomed Inform 63:45–53CrossRefGoogle Scholar
  44. Rodrigues JPC (2016) Mobile health technologies and applications. e-Health Systems, pp 123–139CrossRefGoogle Scholar
  45. Sharma P, Kaur PD (2017) Effectiveness of web-based social sensing in health information dissemination—a review. Telemat Inform 34(1):194–219CrossRefGoogle Scholar
  46. Spiess et al P (2009) SOA-based integration of the internet of things in enterprise services. In: IEEE international conference on web services, pp 968–975Google Scholar
  47. Sprites P, Glymour CN, Scheines R (2000) Caustion, prediction and search, vol 81. MIT Press, CambridgeGoogle Scholar
  48. 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 8:1–33Google Scholar
  49. Swaroop KN, Chandu K, Gorrepotu R, Deb S (2019) A health monitoring system for vital signs using IoT. Internet Things 5:116–129CrossRefGoogle Scholar
  50. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol) 58(1):267–288MathSciNetzbMATHGoogle Scholar
  51. Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Los AltoszbMATHGoogle Scholar
  52. Zadjabbari B, Wongthongtham P, Hussain FK (2010) Ontology based approach in knowledge sharing measurements. J Univers Comput Sci 16(6):956–982Google Scholar
  53. 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 9:66Google Scholar
  54. 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 MedGoogle Scholar
  55. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc B 67(Part 2):301–320MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Guru Nanak Dev UniversityJalandharIndia

Personalised recommendations