Gait Data-Driven Analysis of Parkinson’s Disease Using Machine Learning

Authors

DOI:

https://doi.org/10.4108/eetpht.10.5467

Keywords:

Parkinson's disease, VGRF, Machine Learning, Dual Tasking, RAS, Treadmill Walking

Abstract

INTRODUCTION: Parkinson's disease is a progressive and complex neurological condition that mostly affects coordination and motor control. Parkinson's disease is most commonly associated with its motor symptoms, which include tremors, bradykinesia (slowness of movement), rigidity, and postural instability.

OBJECTIVES: Determine any minor alterations in walking patterns that could be early signs of Parkinson's disease. Track the course of Parkinson's disease over time by using gait data.

METHODS: In this study, we applied three types of VGRF datasets ("Dual Tasking, RAS, and Treadmill Walking") and    developed an ML-based model using six different classifier methods. The datasets were analysed using 16 sensors, of which 8 were applied to each foot and the total pressure of the left and right foot. The aforementioned three distinct gait patterns movement disorders were the sources of the dataset. The gait signals dataset benefited by the participant demographic data. 

RESULTS: Then, we passed the outcome of applying the model and measuring performance through a cross-validation operator to check the accuracy and decision-making of the five algorithms i) Deep Learning, ii) Neural Networks, iii) Support Vector Machine (SVM), iv) Gradient Boost Tree (GBT), v) Random Forest”. The following findings compare the effectiveness of the various algorithms utilized and the observed PD very well.

CONCLUSION: The different ML classifier algorithms demonstrated good detection capability with different accuracy. Our proposed ensemble model is superior to compare with the existing models. Because we can observe the proposed ensemble model result and accuracy better than the other classifier model. The other classifier model’s highest accuracy is 92.08% whereas our ensemble model got 92.31%. So, it has proved that our proposed ensemble model is excellent and robust.

Downloads

Download data is not yet available.

References

Parkinson J. (1817). An Essay on the Shaking Palsy. London: Sherwood, Neely, and Jones.

Kalia, L.V., Lang, A.E. (2015). Parkinson's disease. Lancet., 386(9996), 896-912. doi:10.1016/S0140-6736(14)61393-3 DOI: https://doi.org/10.1016/S0140-6736(14)61393-3

Chaudhuri, K. R, Odin, P., Antonini, A., Martinez-Martin, P. (2011). Parkinson's disease: the non-motor issues. Parkinsonism Relat Disord. 17(10), 717-723. doi:10.1016/j.parkreldis.2011.08.018 DOI: https://doi.org/10.1016/j.parkreldis.2011.02.018

Jankovic., J. (2008). Parkinson's disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry, 79(4), 368-376. doi:10.1136/jnnp.2007.131045 DOI: https://doi.org/10.1136/jnnp.2007.131045

Alissa, M. (2021). Parkinson’s Disease Diagnosis Using Deep Learning. arXiv:2101.05631

Burke, R.E., O’Malley, K. (2013). Axon degeneration in Parkinson’s disease. Exp. Neurol. 246, 72–83. DOI: https://doi.org/10.1016/j.expneurol.2012.01.011

Gunduz, H. (2019). Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets. IEEE Access, 7, 115540–115551. DOI: https://doi.org/10.1109/ACCESS.2019.2936564

Tien I., Glaser S. D., Aminoff M. J. (2010). Characterization of gait abnormalities in Parkinson’s disease using a wireless inertial sensor system. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, 3353–3356. DOI: https://doi.org/10.1109/IEMBS.2010.5627904

Barth J., Klucken J., Kugler P., Kammerer T., Steidl R., Winkler J., Hornegger J., Eskofier B. (2011). Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson’s disease. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 868–871. DOI: https://doi.org/10.1109/IEMBS.2011.6090226

Procházka A., Vyšata O., Vališ M., Upa O., Schätz M., Mařík V. (2015). Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect. Digit. Signal Process. A Rev. J., 47, 169–177. doi: 10.1016/j.dsp.2015.05.011. DOI: https://doi.org/10.1016/j.dsp.2015.05.011

Alam M.N., Garg A., Munia T.T.K., Fazel-Rezai R., Tavakolian K. (2017). Vertical ground reaction force marker for Parkinson’s disease. PLoS ONE., 12, e0175951. doi: 10.1371/journal.pone.0175951. DOI: https://doi.org/10.1371/journal.pone.0175951

Samà A., Pérez-López C., Rodríguez-Martín D., Català A., Moreno-Aróstegui J.M., Cabestany J., de Mingo E., Rodríguez-Molinero A. (2017). Estimating bradykinesia severity in Parkinson’s disease by analysing gait through a waist-worn sensor. Comput. Biol. Med., 84, 114–123. doi: 10.1016/j.compbiomed.2017.03.020 DOI: https://doi.org/10.1016/j.compbiomed.2017.03.020

Arora S., Venkataraman V., Donohue S., Biglan K.M., Dorsey E.R., Little M.A. (2014). High accuracy discrimination of Parkinson’s disease participants from healthy controls using smartphones. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3641–3644. DOI: https://doi.org/10.1109/ICASSP.2014.6854280

M. Svehlík, E.B. Zwick, G. Steinwender, et al. (2009). Gait analysis in patients with Parkinson's disease off dopaminergic therapy. Arch Phys Med Rehabil, 90,1880-1886. doi: 10.1016/j.apmr.2009.06.017 DOI: https://doi.org/10.1016/j.apmr.2009.06.017

Ozkan, H. (2016). A comparison of classification methods for telediagnosis of Parkinson’s disease. Entropy 18, 115. doi: 10.3390/e18040115 DOI: https://doi.org/10.3390/e18040115

Abdulhay, E., Arunkumar, N., Narasimhan, K., Vellaiappan, E., and Venkatraman, V. (2018). Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson's disease. Future Gener. Comput. Syst. 83, 366–373. doi: 10.1016/j.future.2018.02.009 DOI: https://doi.org/10.1016/j.future.2018.02.009

Buongiorno, D., Bortone, I., Cascarano, G. D., Trotta, G. F., Brunetti, A., and Bevilacqua, V. (2019). A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s disease. BMC Med. Inform. Decis. Mak., 19, 243. doi: 10.1186/s12911-019-0987-5 DOI: https://doi.org/10.1186/s12911-019-0987-5

Varrecchia, T., Castiglia, S. F., Ranavolo, A., Conte, C., Tatarelli, A., Coppola, G., et al. (2021). An artificial neural network approach to detect the presence and severity of Parkinson’s disease via gait parameters. PLoS One 16, e0244396. doi: 10.1371/journal.pone.0244396 DOI: https://doi.org/10.1371/journal.pone.0244396

Frenkel-Toledo S., Giladi N., Peretz C., Herman T., Gruendlinger L., Hausdorff J.M. (2005). Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson’s disease. Mov. Disord., 20, 1109–1114. doi: 10.1002/mds.20507 DOI: https://doi.org/10.1002/mds.20507

Downloads

Published

19-03-2024

How to Cite

1.
Panda A, Bhuyan P. Gait Data-Driven Analysis of Parkinson’s Disease Using Machine Learning. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 19 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5467