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Intelligent cyber-physical system for an efficient detection of Parkinson disease using fog computing

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Abstract

Parkinson’s disease is one of the notable neurodegenerative disorders caused by insufficient production of dopamine which damages the motor skills and voice. Advancement of the Internet of Things (IoT) has fuelled the development of healthcare systems. In this article, we propose an intelligent system for detecting Parkinson’s disease to provide proper medication by analysing voice samples. Instead of relying on limited storage capacity and computational resources of IoT, the recent healthcare systems take advantages of the cloud server. On the other hand, the utilization of cloud computing incurs the issues of data privacy and additional communication costs to the healthcare systems. To address this issue, we propose to utilize Fog computing as a midway layer between end devices and the cloud server. The proposed system employs the combinatorial Fuzzy K-nearest Neighbor and Case-based Reasoning classifier for the better classification of the Parkinson patients from healthy individuals. On the detection of abnormality, the proposed healthcare system is designed to generate an immediate alert to the patient. The proposed system is experimentally evaluated on the UCI-Parkinson dataset, and the results reveal the improved performance of our system over baseline approaches.

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References

  1. Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59

    Google Scholar 

  2. Abawajy JH, Hassan MM (2017) Federated internet of things and cloud computing pervasive patient health monitoring system. IEEE Commun Mag 55(1):48–53

    Google Scholar 

  3. Abbas A, Khan SU (2014) A review on the state-of-the-art privacy-preserving approaches in the e-health clouds. IEEE Journal of Biomedical and Health Informatics 18(4):1431–1441

    Google Scholar 

  4. Al Mamun KA, Alhussein M, Sailunaz K, Islam MS (2017) Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications. Futur Gener Comput Syst 66:36–47

    Google Scholar 

  5. Alhussein M (2017) Monitoring Parkinson’s disease in smart cities. IEEE Access 5:19835–19841

    Google Scholar 

  6. Almogren A (2018) An automated and intelligent Parkinson disease monitoring system using wearable computing and cloud technology. Cluster Computing, 1–8

  7. Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2009) Above the clouds: A berkeley view of cloud computing (Vol. 4, pp. 506–522). Technical Report UCB/EECS-2009-28, EECS Department, University of California, Berkeley

  8. Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, Little MA (2015) Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord 21(6):650–653

    Google Scholar 

  9. Arunkumar S, Subramaniyaswamy V, Karthikeyan B, Saravanan P, Logesh R (2018) Meta-data based secret image sharing application for different sized biomedical images. Biomed Res 29:394–398

    Google Scholar 

  10. Bakar ZA, Ispawi DI, Ibrahim NF, Tahir NM (2012) Classification of Parkinson's disease based on Multilayer Perceptrons (MLPs) Neural Network and ANOVA as a feature extraction. In Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on (pp. 63–67). IEEE

  11. Bhattacharya I, Bhatia MPS (2010) SVM classification to distinguish Parkinson disease patients. In Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India (p. 14). ACM

  12. Bohanec M, Miljković D, Valmarska A, Mileva Boshkoska B, Gasparoli E, Gentile G, Koutsikos K, Marcante A, Antonini A, Gatsios D, Rigas G (2018) A decision support system for Parkinson disease management: expert models for suggesting medication change. J Decis Syst 27(sup1):164–172

    Google Scholar 

  13. Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13–16). ACM

  14. Chaabouni S, Benois-Pineau J, Tison F, Amar CB, Zemmari A (2017) Prediction of visual attention with deep CNN on artificially degraded videos for studies of attention of patients with dementia. Multimedia Tools and Applications 76(21):22527–22546

    Google Scholar 

  15. Costanzo A, Faro A, Giordano D, Pino C (2016) Mobile cyber physical systems for health care: Functions, ambient ontology and e-diagnostics. In Consumer Communications & Networking Conference (CCNC), 2016 13th IEEE Annual (pp. 972–975). IEEE

  16. Das R (2010) A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Syst Appl 37(2):1568–1572

    Google Scholar 

  17. Dauer W, Przedborski S (2003) Parkinson’s disease: mechanisms and models. Neuron 39(6):889–909

    Google Scholar 

  18. Ene M (2008) Neural network-based approach to discriminate healthy people from those with Parkinson's disease. Annals of the University of Craiova-Mathematics and Computer Science Series 35:112–116

    MathSciNet  MATH  Google Scholar 

  19. Engel AK, Moll CK, Fried I, Ojemann GA (2005) Invasive recordings from the human brain: clinical insights and beyond. Nat Rev Neurosci 6(1):35

    Google Scholar 

  20. Gil D, Manuel DJ (2009) Diagnosing Parkinson by using artificial neural networks and support vector machines. Global Journal of Computer Science and Technology 9(4)

  21. Goetz CG, Stebbins GT, Wolff D, DeLeeuw W, Bronte-Stewart H, Elble R, Hallett M, Nutt J, Ramig L, Sanger T, Wu AD (2009) Testing objective measures of motor impairment in early Parkinson's disease: feasibility study of an at-home testing device. Mov Disord 24(4):551–556

    Google Scholar 

  22. Hossain MS, Muhammad G (2014) Cloud-based collaborative media service framework for healthcare. International Journal of Distributed Sensor Networks 10(3):858712

    Google Scholar 

  23. Hossain MS, Muhammad G (2016) Cloud-assisted industrial internet of things (iiot)–enabled framework for health monitoring. Comput Netw 101:192–202

    Google Scholar 

  24. Indragandhi V, Subramaniyaswamy V, Logesh R (2017) Resources, configurations, and soft computing techniques for power management and control of PV/wind hybrid system. Renew Sust Energ Rev 69:129–143

    Google Scholar 

  25. Indragandhi V, Subramaniyaswamy V, Logesh R (2017) Topological review and analysis of DC-DC boost converters. Journal of Engineering Science and Technology 12(6):1541–1567

    Google Scholar 

  26. Indragandhi V, Logesh R, Subramaniyaswamy V, Vijayakumar V, Siarry P, Uden L (2018) Multi-objective optimization and energy management in renewable based AC/DC microgrid. Computers & Electrical Engineering

  27. Islam MS, Parvez I, Deng H, Goswami P (2014) Performance comparison of heterogeneous classifiers for detection of Parkinson's disease using voice disorder (dysphonia). In Informatics, Electronics & Vision (ICIEV), 2014 International Conference on (pp. 1–7). IEEE

  28. Jagadeeswari V, Subramaniyaswamy V, Logesh R, Vijayakumar V (2018) A study on medical internet of things and big data in personalized healthcare system. Health information science and systems 6(1):14

    Google Scholar 

  29. Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, Edwards MJ, Bhatia KP (2016) Developing a tool for remote digital assessment of Parkinson's disease. Movement Disorders Clinical Practice 3(1):59–64

    Google Scholar 

  30. Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, and cybernetics 4:580–585

    Google Scholar 

  31. Khemphila A, Boonjing V (2012) Parkinsons disease classification using neural network and feature selection. World Acad Sci Eng Technol 64:15–18

    Google Scholar 

  32. Kim J, Nasir M, Gupta R, Segbroeck MV, Bone D, Black MP, Skordilis ZI, Yang Z, Georgiou PG, Narayanan SS (2015) Automatic estimation of Parkinson's disease severity from diverse speech tasks. In Sixteenth Annual Conference of the International Speech Communication Association

  33. Kringelbach ML, Jenkinson N, Owen SL, Aziz TZ (2007) Translational principles of deep brain stimulation. Nat Rev Neurosci 8(8):623

    Google Scholar 

  34. Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO (2009) Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Trans Biomed Eng 56(4):1015–1022

    Google Scholar 

  35. Logesh R, Subramaniyaswamy V (2017) Learning Recency and Inferring Associations in Location Based Social Network for Emotion Induced Point-of-Interest Recommendation. Journal of Information Science & Engineering, 33(6)

  36. Logesh R, Subramaniyaswamy V (2017) A reliable point of interest recommendation based on trust relevancy between users. Wirel Pers Commun 97(2):2751–2780

    Google Scholar 

  37. Logesh R, Subramaniyaswamy V (2019) Exploring hybrid recommender Systems for Personalized Travel Applications. In: In cognitive informatics and soft computing. Springer, Singapore, pp 535–544

    Google Scholar 

  38. Logesh R, Subramaniyaswamy V, Malathi D, Senthilselvan N, Sasikumar A, Saravanan P (2017) Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback. Biomed Res 28(13):5646–5650

    Google Scholar 

  39. Logesh R, Subramaniyaswamy V, Vijayakumar V, Gao XZ, Indragandhi V (2018) A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Futur Gener Comput Syst 83:653–673

    Google Scholar 

  40. Logesh R, Subramaniyaswamy V, Vijayakumar V, Li X (2018) Efficient User Profiling Based Intelligent Travel Recommender System for Individual and Group of Users. Mobile Networks and Applications, 1–16

  41. Logesh R, Subramaniyaswamy V, Vijayakumar V (2018) A personalised travel recommender system utilising social network profile and accurate GPS data. Electronic Government, an International Journal 14(1):90–113

    Google Scholar 

  42. Lounis A, Hadjidj A, Bouabdallah A, Challal Y (2016) Healing on the cloud: secure cloud architecture for medical wireless sensor networks. Futur Gener Comput Syst 55:266–277

    Google Scholar 

  43. Mantri S, Fullard ME, Duda JE, Morley JF (2018) Physical activity in early Parkinson disease. Journal of Parkinson's disease 8(1):107–111

    Google Scholar 

  44. Muhammad G (2015) Automatic speech recogitio usig iterlaced derivative patter for cloud based healthcare system. Clust Comput 18(2):795–802

    MathSciNet  Google Scholar 

  45. Nelson ME, Rejeski WJ, Blair SN, Duncan PW, Judge JO, King AC, Macera CA, Castaneda-Sceppa C (2007) Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. Circulation 116(9):1094

    Google Scholar 

  46. Pan D, Dhall R, Lieberman A, Petitti DB (2015) A mobile cloud-based Parkinson’s disease assessment system for home-based monitoring. JMIR mHealth and uHealth, 3(1)

    Google Scholar 

  47. Parkinson Dataset (2018) Last accessed on 9th April. [online]. Available: https://archive.ics.uci.edu/ml/datasets/Parkinsons+Telemonitoring/

  48. Parkinson Dataset (2018) Last accessed on 6th April 2018. [online]. Available: http://archive.ics.uci.edu/ml/datasets/Parkinsons/

  49. Pogorelc B, Bosnić Z, Gams M (2012) Automatic recognition of gait-related health problems in the elderly using machine learning. Multimedia Tools and Applications 58(2):333–354

    Google Scholar 

  50. Putri FT, Ariyanto M, Caesarendra W, Ismail R, Pambudi KA, Pasmanasari ED (2018) Low cost Parkinson’s disease early detection and classification based on voice and electromyography signal. In: In computational intelligence for pattern recognition. Springer, Cham, pp 397–426

    Google Scholar 

  51. Ramani RG, Sivagami G (2011) Parkinson disease classification using data mining algorithms. International journal of computer applications 32(9):17–22

    Google Scholar 

  52. Rani KU, Holi MS (2012) Analysis of speech characteristics of neurological diseases and their classification. In Computing Communication & Networking Technologies (ICCCNT), 2012 Third International Conference on (pp. 1–6). IEEE

  53. Ravi L, Vairavasundaram S (2016) A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Computational intelligence and neuroscience 2016:7

    Google Scholar 

  54. Robichaud JA, Pfann KD, Leurgans S, Vaillancourt DE, Comella CL, Corcos DM (2009) Variability of EMG patterns: a potential neurophysiological marker of Parkinson’s disease? Clin Neurophysiol 120(2):390–397

    Google Scholar 

  55. Sakar CO, Kursun O (2010) Telediagnosis of Parkinson’s disease using measurements of dysphonia. J Med Syst 34(4):591–599

    Google Scholar 

  56. Shirvan RA, Tahami E (2011) Voice analysis for detecting Parkinson's disease using genetic algorithm and KNN classification method. In Biomedical Engineering (ICBME), 2011 18th Iranian Conference of (pp. 278–283). IEEE

  57. Stamate C, Magoulas GD, Kueppers S, Nomikou E, Daskalopoulos I, Jha A, Pons JS, Rothwell J, Luchini MU, Moussouri T, Iannone M (2018) The cloudUPDRS app: a medical device for the clinical assessment of Parkinson’s disease. Pervasive and Mobile Computing 43:146–166

    Google Scholar 

  58. Subramaniyaswamy V, Logesh R (2017) Adaptive KNN based recommender system through Mining of User Preferences. Wirel Pers Commun 97(2):2229–2247

    Google Scholar 

  59. Subramaniyaswamy V, Logesh R (2018) Applying Semantic Relations for Automatic Topic Ontology Construction. In Developments and Trends in Intelligent Technologies and Smart Systems (pp. 48–77). IGI Global

  60. Subramaniyaswamy V, Vijayakumar V, Logesh R, Indragandhi V (2015) Unstructured data analysis on big data using map reduce. Procedia Computer Science 50:456–465

    Google Scholar 

  61. Subramaniyaswamy V, Logesh R, Vijayakumar V, Indragandhi V (2015) Automated message filtering system in online social network. Procedia Computer Science 50:466–475

    Google Scholar 

  62. Subramaniyaswamy V, Vijayakumar V, Logesh R, Indragandhi V (2015) Intelligent travel recommendation system by mining attributes from community contributed photos. Procedia Computer Science 50:447–455

    Google Scholar 

  63. Subramaniyaswamy V, Vaibhav MV, Prasad RV, Logesh R (2017) Predicting movie box office success using multiple regression and SVM. In 2017 International Conference on Intelligent Sustainable Systems (ICISS) (pp. 182–186). IEEE

  64. Subramaniyaswamy V, Logesh R, Abejith M, Umasankar S, Umamakeswari A (2017) Sentiment analysis of tweets for estimating criticality and security of events. Journal of Organizational and End User Computing (JOEUC) 29(4):51–71

    Google Scholar 

  65. Subramaniyaswamy V, Logesh R, Chandrashekhar M, Challa A, Vijayakumar V (2017) A personalised movie recommendation system based on collaborative filtering. International Journal of High Performance Computing and Networking 10(1–2):54–63

    Google Scholar 

  66. Subramaniyaswamy V, Manogaran G, Logesh R, Vijayakumar V, Chilamkurti N, Malathi D, Senthilselvan N (2018) An ontology-driven personalized food recommendation in IoT-based healthcare system. The Journal of Supercomputing, 1–33

  67. Subramaniyaswamy V, Logesh R, Indragandhi V (2018) Intelligent sports commentary recommendation system for individual cricket players. International Journal of Advanced Intelligence Paradigms 10(1–2):103–117

    Google Scholar 

  68. Sujatha J, Rajagopalan SP (2017) Performance evaluation of machine learning algorithms in the classification of Parkinson disease using voice attributes. Int J Appl Eng Res 12(21):10669–10675

    Google Scholar 

  69. Tsanas A, Little MA, McSharry PE, Ramig LO (2010) Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests. IEEE Trans Biomed Eng 57(4):884–893

    Google Scholar 

  70. Vairavasundaram S, Varadharajan V, Vairavasundaram I, Ravi L (2015) Data mining-based tag recommendation system: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5(3):87–112

    Google Scholar 

  71. Vijayakumar V, Subramaniyaswamy V, Logesh R, Sivapathi A (2018) Effective Knowledge Based Recommeder System for Tailored Multiple Point of Interest Recommendation. International Journal of Web Portals

  72. Wooten GF, Currie LJ, Bovbjerg VE, Lee JK, Patrie J (2004) Are men at greater risk for Parkinson’s disease than women? J Neurol Neurosurg Psychiatry 75(4):637–639

    Google Scholar 

  73. Yan A, Yu H, Wang D (2017) Case-based reasoning classifier based on learning pseudo metric retrieval. Expert Syst Appl 89:91–98

    Google Scholar 

  74. Zheng YL, Ding XR, Poon CCY, Lo BPL, Zhang H, Zhou XL, Yang GZ, Zhao N, Zhang YT (2014) Unobtrusive sensing and wearable devices for health informatics. IEEE Trans Biomed Eng 61(5):1538–1554

    Google Scholar 

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Acknowledgements

The authors express their gratitude to SASTRA Deemed University, Thanjavur, India for the financial support and infrastructural facilities provided to carry out this research work.

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    Malathi Devarajan & Logesh Ravi

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Devarajan, M., Ravi, L. Intelligent cyber-physical system for an efficient detection of Parkinson disease using fog computing. Multimed Tools Appl 78, 32695–32719 (2019). https://doi.org/10.1007/s11042-018-6898-0

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