Authors : Arnab Biswas; Shamayita Mukherjee; Arnab Maji; Ayan Manna; Shouvik Sarkar

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/3525xz5y

Scribd : https://tinyurl.com/3hcr9ckj

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG995

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In this paper, we focus on designing and implementing a wearable device for detecting Parkinson's disease (PD) symptoms by analyzing resting tremors and abnormal muscle activity which contribute to PD combining gyroscope and electromyogram(EMG) analysis. Using advanced sensor technology, real-time data about movement and muscle activity is captured by the device. Here, we outline a hardware framework for optimizing data acquisition by identifying sensors to be used, their placement and integration strategies. In order to analyze data, machine learning algorithms are used to distinguish between tremors and muscle activity that are specific to Parkinson's disease and normal movements using classification technique. By enabling proactive healthcare interventions and customized patient management strategies, the proposed device represents a promising tool for the detection of early-stage Parkinson's disease.

Keywords : Parkinson’s Disease(PD), Resting Tremor, Muscle Activity, Gyroscope, Electromyogram(EMG).

References :

1. Prasad R., Babu S., Siddaiah N., Rao K. A review on techniques for diagnosing and monitoring patients with parkinson’s disease. J. Biosens. Bioelectron. 2016;7:2.
2. Reeve A., Simcox E., Turnbull D. Ageing and Parkinson’s disease: Why is advancing age the biggest risk factor? Ageing Res. Rev. 2014;14:19–30. doi: 10.1016/j.arr.2014.01.004.
3. Wirdefeldt K., Adami H.-O., Cole P., Trichopoulos D., Mandel J. Epidemiology and etiology of Parkinson’s disease: A review of the evidence. Eur. J. Epidemiol. 2011;26:1–58. doi: 10.1007/s10654-011-9581-6.
4. Sveinbjornsdottir S. The clinical symptoms of Parkinson’s disease. J. Neurochem. 2016;139:318–324. doi: 10.1111/jnc.13691.
5. D. Hirtz, D. J. Thurman , K. Gwinn-Hardy, M. Mohamed, A. R. Chaudhuri, and R. Zalutsky, "How common are the "common" neurologic disorders?," Neurology, vol. 68, no. 5, pp. 326-337, 2007.
6. K. Badiavas, E. Molyvda, I. Iakovou, M. Tsolaki, K. Psarrakos, and N. Karatzas, "SPECT imaging evaluation in movement disorders: far beyond visual assessment," vol. 38, no. 4, pp. 764-773, 2011.
7. A. J. Hughes, S. E. Daniel, L. Kilford, and A. J. Lees, "Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico pathological study of 100 cases," Journal of Neurology, Neurosurgery, and Psychiatry, vol. 55, no. 3, pp. 181–184, 1992.
8. Palmes P., Ang W.T., Widjaja F., Tan L.C., Au W.-L. Pattern Mining of Multichannel sEMG for Tremor Classification. IEEE Trans. Biomed. Eng. 2010;57:2795–2805. doi: 10.1109/TBME.2010.2076810.
9. Hssayeni M.D., Jimenez-Shahed J., Burack M.A., Ghoraani B. Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements. Sensors. 2019;19:4215. doi: 10.3390/s19194215.
10. Mailankody P., Thennarasu K., Nagaraju B.C., Yadav R., Pal P.K. Re-emergent tremor in Parkinson’s disease: A clinical and electromyographic study. J. Neurol. Sci. 2016;366:33–36. doi: 10.1016/j.jns.2016.04.041.
11. Rovini E., Maremmani C., Cavallo F. How Wearable Sensors Can Support Parkinson’s Disease Diagnosis and Treatment: A Systematic Review. Front. Neurosci. 2017;11:555. doi: 10.3389/fnins.2017.00555.
12. López-Blanco R., Velasco M.A., Méndez-Guerrero A., Romero J.P., Del Castillo M.D., Serrano J.I., Rocon E., Benito-León J. Smartwatch for the analysis of rest tremor in patients with Parkinson’s disease. J. Neurol. Sci. 2019;401:37–42. doi: 10.1016/j.jns.2019.04.011.
13. K. Badiavas, E. Molyvda, I. Iakovou, M. Tsolaki, K. Psarrakos, and N. Karatzas, "SPECT imaging evaluation in movement disorders: far beyond visual assessment," vol. 38, no. 4, pp. 764-773, 2011.
14. S. Wold, K. Esbensen, and P. Geladi, "Principal component analysis," Chemometrics and Intelligent Laboratory Systems, vol. 2, no. 1-3, pp. 37-52, 1987.
15. M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, and B. Scholkopf, "Support vector machines," IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18 - 28, 1998
16. Kubota K.J., Chen J.A., Little M.A. Machine learning for large-scale wearable sensor data in Parkinson’s disease: Concepts, promises, pitfalls, and futures. Mov. Disord. 2016;31:1314–1326. doi: 10.1002/mds.26693.
17. Maetzler W. A clinical view on the development of technology-based tools in managing Parkinson’s disease. Mov. Disord. 2016;31:1263–1271. doi: 10.1002/mds.26673.
18. Matias R., Paixão V., Bouça R., Ferreira J.J. A Perspective on Wearable Sensor Measurements and Data Science for Parkinson’s Disease. Front. Neurol. 2017;8:8. doi: 10.3389/fneur.2017.00677.
19. Li L., Jamieson K., Desalvo G., Rostamizadeh A., Talwalkar A. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. arXiv. 20181603.06560
20. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995;20:273–297. doi: 10.1007/BF00994018.