The Development of an IoT-based Cyber-Physical System for Parkinson’s Disease Patients Using ML Prediction Algorithm

Abstract

Most people in the eternal world we reawaken to are stressed and wrecked with diseases because of genetic, environmental, and emotional causes. There are many incurable diseases, but there are also ways to treat them that are hopeful and even life-extending. As a result, it is hypothesized that the prototype will limit the capacities of people with Parkinson's disease. It's a disease that causes degeneration in the brain and spinal cord. The most noticeable signs are trembling, stiffness, slowness of movement, and difficulty walking. Depression and anxiety are symptoms of dementia, a form of brain condition that worsens as a result of this disease. It also makes it hard to get to sleep at night. For some illnesses, we lack the diagnostic instrument necessary to determine the precise status of many conditions. Wearable sensors can diagnose Parkinson's disease (PD) in its earliest stages, when treatment is most effective. Brain wave detection and other forms of human chronicle are detected with effective sensors in the proposed system for Parkinson disease, demonstrating the improvement of the patient's central nervous system. Here, we use machine learning with a prediction algorithm to keep a close eye on the patient's progress and store the data in the cloud automatically so that we can reliably get an analysis of the patient's performance at any time. For better classification results, we create a prediction system based on the fuzzy k-nearest neighbor (FKNN). The suggested system may monitor patients in more ways than just by measuring vitals like heart rate, blood pressure, and temperature.

Keywords

• Parkinson disease
• Fuzzy K-Nearest Neighbour
• Sensors
• Cloud platform.

Supporting Institution

Not applicable

Project Number

Not applicable

Ethical Statement

Not applicable

References

1. [1] H. Xu, W. Yu, D. Griffith, and N. Golmie, ‘‘A survey on industrial internet of things: A cyber-physical systems perspective,’’ IEEE Access, vol. 6, pp. 78 238– 78 259, 2018.
2. [2] A. J. Espay, P. Bonato, F. B. Nahab, W. Maetzler, J. M. Dean, J. Klucken, B. M. Eskofier, A. Merola, F. Horak, A. E. Lang, R. Reilmann, J. Giuffrida, A. Nieuwboer, M. Horne, M. A. Little, I. Litvan, T. Simuni, E. R. Dorsey, M. A. Burack, K. Kubota, A. Kamondi, C. Godinho, J.-F. Daneault, G. Mitsi, L. Krinke, J. M. Hausdorff, B. R. Bloem, and S. Papapetropoulos, ‘‘Technology in parkinson’s disease: Challenges and opportunities,’’ Movement Disorders, vol. 31, pp. 1272–1282, 9 2016.
3. [3] K. N. R. Challa, V. S. Pagolu, G. Panda, and B. Majhi, ‘‘An improved approach for prediction of parkinson’s disease using machine learning techniques.’’ IEEE, 10 2016, pp. 1446–1451.
4. [4] R. Djaldetti, I. Ziv, and E. Melamed, ‘‘The mystery of motor asymmetry in parkinson’s disease,’’ The Lancet Neurology, vol. 5, pp. 796–802, 9 2006.
5. [5] K. R. Chaudhuri, P. Odin, A. Antonini, and P. Martinez-Martin, ‘‘Parkinson’s disease: The non-motor issues,’’ Parkinsonism & Related Disorders, vol. 17, pp. 717–723, 12 2011.
6. [6] R. BENECKE, J. C. ROTHWELL, J. P. R. DICK, B. L. DAY, and C. D. MARSDEN, ‘‘Disturbance of sequential movements in patients with parkinson’s disease,’’ Brain, vol. 110, pp. 361–379, 1987.
7. [7] N. Miller, E. Noble, D. Jones, and D. Burn, ‘‘Life with communication changes in parkinson’s disease,’’ Age and Ageing, vol. 35, pp. 235–239, 5 2006.
8. [8] H. Braak and E. Braak, ‘‘Pathoanatomy of parkinson’s disease,’’ Journal of Neurology, vol. 247, pp. II3–II10, 4 2000.

9. [9] A. C. Belin and M. Westerlund, ‘‘Parkinson’s disease: A genetic perspective,’’ The FEBS Journal, vol. 275, pp. 1377–1383, 4 2008.
10. [10] A. Ascherio and M. A. Schwarzschild, ‘‘The epidemiology of parkinson’s disease: risk factors and prevention,’’ The Lancet Neurology, vol. 15, pp. 1257–1272, 11 2016.
11. [11] I. G. McKeith and D. Burn, ‘‘Spectrum of parkinson’s disease, parkinson’s dementia, and lewy body dementia,’’ Neurologic Clinics, vol. 18, pp. 865–883, 11 2000.
12. [12] C. L. Comella, ‘‘Sleep disorders in parkinson’s disease: An overview,’’ Movement Disorders, vol. 22, pp. S367–S373, 2007.
13. [13] K. M. Smith and N. Dahodwala, ‘‘Sex differences in parkinson’s disease and other movement disorders,’’ Experimental Neurology, vol. 259, pp. 44–56, 9 2014.
14. [14] D. K. Simon, C. M. Tanner, and P. Brundin, ‘‘Parkinson disease epidemiology, pathology, genetics, and pathophysiology,’’ Clinics in Geriatric Medicine, vol. 36, pp. 1–12, 2 2020.
15. [15] V. Annese, G. Mezzina, V. Gallo, V. Scarola, and D. D. Venuto, ‘‘Wearable platform for automatic recognition of parkinson disease by muscular implication monitoring.’’ IEEE, 6 2017, pp. 150–154.
16. [16] P. Angeles, Y. Tai, N. Pavese, S. Wilson, and R. Vaidyanathan, ‘‘Automated assessment of symptom severity changes during deep brain stimulation (dbs) therapy for parkinson’s disease.’’ IEEE, 7 2017, pp. 1512–1517.
17. [17] A. Wagner, N. Fixler, and Y. S. Resheff, ‘‘A wavelet-based approach to monitoring parkinson’s disease symptoms.’’ IEEE, 3 2017, pp. 5980–5984.
18. [18] V. Ramji, M. Hssayeni, M. A. Burack, and B. Ghoraani, ‘‘Parkinson’s disease medication state management using data fusion of wearable sensors.’’ IEEE, 2017, pp. 193–196.
19. [19] H. Ma, T. Tan, H. Zhou, and T. Gao, ‘‘Support vector machine-recursive feature elimination for the diagnosis of parkinson disease based on speech analysis.’’ IEEE, 12 2016, pp. 34–40.
20. [20] S. Afroz, T. M. N. U. Akhund, T. Khan, M. U. Hasan, R. Jesmin, and M. M. Sarker, Internet of Sensing Things-Based Machine Learning Approach to Predict Parkinson, 2024, pp. 651–660.
21. [21] M. Vadovsky and J. Paralic, ‘‘Parkinson’s disease patients classification based on the speech signals.’’ IEEE, 1 2017, pp. 000 321–000 326.
22. [22] C. Godinho, V. Ferret-Sena, J. Brito, F. Melo, and M. S. Dias, ‘‘Postural behavior and parkinson’s disease severity.’’ IEEE, 12 2016, pp. 1–6.
23. [23] S. Shetty and Y. S. Rao, ‘‘Svm based machine learning approach to identify parkinson’s disease using gait analysis.’’ IEEE, 8 2016, pp. 1–5.
24. [24] M. G. Krokidis, G. N. Dimitrakopoulos, A. G. Vrahatis, C. Tzouvelekis, D. Drakoulis, F. Papavassileiou, T. P. Exarchos, and P. Vlamos, ‘‘A sensor-based perspective in early-stage parkinson’s disease: Current state and the need for machine learning processes,’’ Sensors, vol. 22, p. 409, 1 2022.
25. [25] C. F. Pasluosta, H. Gassner, J. Winkler, J. Klucken, and B. M. Eskofier, ‘‘An emerging era in the management of parkinson’s disease: Wearable technologies and the internet of things,’’ IEEE Journal of Biomedical and Health Informatics, vol. 19, pp. 1873–1881, 11 2015.
26. [26] O. d’Angelis, L. D. Biase, L. Vollero, and M. Merone, ‘‘Iot architecture for continuous long term monitoring: Parkinson’s disease case study,’’ Internet of Things, vol. 20, p. 100614, 11 2022.
27. [27] V. Sachnev and H. J. Kim, ‘‘Parkinson disease classification based on binary coded genetic algorithm and extreme learning machine.’’ IEEE, 4 2014, pp. 1–6.
28. [28] Y.-W. Bai, C.-C. Chan, and C.-H. Yu, ‘‘Design and implementation of a user interface of a smartphone for the parkinson’s disease patients.’’ IEEE, 1 2015, pp. 257–258.
29. [29] K.-M. Giannakopoulou, I. Roussaki, and K. Demestichas, ‘‘Internet of things technologies and machine learning methods for parkinson’s disease diagnosis, monitoring and management: A systematic review,’’ Sensors, vol. 22, p. 1799, 2 2022.
30. [30] Z. Dong, H. Gu, Y. Wan, W. Zhuang, R. Rojas-Cessa, and E. Rabin, ‘‘Wireless body area sensor network for posture and gait monitoring of individuals with parkinson’s disease.’’ IEEE, 4 2015, pp. 81–86.
31. [31] P. Kraipeerapun and S. Amornsamankul, ‘‘Using stacked generalization and complementary neural networks to predict parkinson’s disease.’’ IEEE, 8 2015, pp. 1290–1294.
32. [32] A. Bourouhou, A. Jilbab, C. Nacir, and A. Hammouch, ‘‘Comparison of classification methods to detect the parkinson disease.’’ IEEE, 5 2016, pp. 421–424.
33. [33] A. Uehara, H. Kawamoto, and Y. Sankai, ‘‘Development of gait assist method for parkinson’s disease patients with fog in walking.’’ IEEE, 9 2016, pp. 1502–1507.
34. [34] A. H. Al-Fatlawi, M. H. Jabardi, and S. H. Ling, ‘‘Efficient diagnosis system for parkinson’s disease using deep belief network.’’ IEEE, 7 2016, pp. 1324–1330.