A systematic approach to diagnose Parkinson’s disease through kinematic features extracted from handwritten drawings


Parkinson’s disease is a slowly progressing neurodegenerative disorder that is not easy to diagnose at the early stages because of delayed symptoms. The most usual ways to diagnose this disease is either by reviewing the medical history of the patient and looking at the computerized tomography scans along with the magnetic resonance imaging by a neurologist or by analyzing the body movements of the patient by the body movement analysts. However, recent research work indicates that Parkinson’s can be effectively diagnosed at an early stage by measuring the changes in handwriting. In this work, the authors have proposed a Parkinson’s disease diagnosis system by analyzing the kinematic features extracted from the handwritten spirals drawn by patients. The publicly available University of California, Irvine Parkinson’s disease spiral drawings using digitized graphics tablet dataset is used in this study. A total of 29 kinematics features are extracted from the dataset. The class imbalance problem in the dataset is handled by the synthetic minority oversampling technique because the dataset is highly imbalanced. Relevant features are selected using the genetic algorithm and mutual information gain feature selection methods. The performance of four classifiers support vector machine, random forest, AdaBoost and XGBoost are analyzed in terms of accuracy, sensitivity, specificity, precision, F-measure, and area under ROC curve. Tenfold cross-validation method is used for validating the results. The combination of mutual information gain feature selection method with AdaBoost classifiers outperforms with 96.02% accuracy.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Code availability

Not applicable.


  1. 1.

    Sakar CO, Serbes G, Gunduz A, Tunc HC, Nizam H, Sakar BE, Tutuncu M, Aydin T, Isenkul ME, Apaydin H (2019) A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl Soft Comput 74:255–263. https://doi.org/10.1016/j.asoc.2018.10.022

    Article  Google Scholar 

  2. 2.

    Tuncer T, Dogan S, Acharya UR (2020) Automated detection of Parkinson’s disease using minimum average maximum tree and singular value decomposition method with vowels. Biocybern Biomed Eng 40(1):211–220. https://doi.org/10.1016/j.bbe.2019.05.006

    Article  Google Scholar 

  3. 3.

    Lamba R, Gulati T, Jain A (2020) Comparative analysis of Parkinson’s disease diagnosis system. Adv Math Sci J 9(6):3399–3406. https://doi.org/10.37418/amsj.9.6.20

    Article  Google Scholar 

  4. 4.

    Emamzadeh FN, Surguchov A (2018) Parkinson’s disease: biomarkers, treatment, and risk factors. Front Neurosci 12:612. https://doi.org/10.3389/fnins.2018.00612

    Article  Google Scholar 

  5. 5.

    Reich SG, Savitt JM (2018) Parkinson disease. Med Clin N Am. https://doi.org/10.1016/j.mcna.2018.10.014

    Article  Google Scholar 

  6. 6.

    Zesiewicz TA, Bezchlibnyk Y, Dohse N, Ghanekar SD (2019) Management of early Parkinson disease. Clin Geriatr Med. https://doi.org/10.1016/j.cger.2019.09.001

    Article  Google Scholar 

  7. 7.

    Drotár P, Mekyska J, Rektorová I, Masarová L, Smékal Z, Faundez-Zanuy M (2016) Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson’s disease. Artif Intell Med 67:39–46. https://doi.org/10.1016/j.artmed.2016.01.004

    Article  Google Scholar 

  8. 8.

    Coronato A (2018) Engineering high quality medical software: regulations, standards, methodologies and tools for certification. Inst Eng Technol (Healthc Technol). https://doi.org/10.1049/PBHE012E

    Article  Google Scholar 

  9. 9.

    Isenkul M, Sakar B, Kursun O (2014) Improved spiral test using digitized graphics tablet for monitoring Parkinson’s disease. In: Proceedings of the international conference on e-health and telemedicine, pp 171–175

  10. 10.

    Pereira CR, Pereira DR, da Silva FA, Hook C, Weber SA, Pereira LA, Papa JP (2015) A step towards the automated diagnosis of Parkinson’s disease: analyzing handwriting movements. In: 2015 IEEE 28th international symposium on computer-based medical systems. IEEE, pp 171–176. https://doi.org/10.1109/CBMS.2015.34

  11. 11.

    Kotsavasiloglou C, Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M (2017) Machine learning-based classification of simple drawing movements in Parkinson’s disease. Biomed Signal Process Control 31:174–180. https://doi.org/10.1016/j.bspc.2016.08.003

    Article  Google Scholar 

  12. 12.

    Zham P, Arjunan SP, Raghav S, Kumar DK (2017) Efficacy of guided spiral drawing in the classification of Parkinson’s disease. IEEE J Biomed Health Inform 22(5):1648–1652. https://doi.org/10.1109/JBHI.2017.2762008

    Article  Google Scholar 

  13. 13.

    Impedovo D, Pirlo G, Vessio G (2018) Dynamic handwriting analysis for supporting earlier Parkinson’s disease diagnosis. Information 9(10):247. https://doi.org/10.3390/info9100247

    Article  Google Scholar 

  14. 14.

    Mucha J, Mekyska J, Faundez-Zanuy M, Lopez-De-Ipina K, Zvoncak V, Galaz Z, Kiska T, Smekal Z, Brabenec L, Rektorova I (2018) Advanced Parkinson's disease dysgraphia analysis based on fractional derivatives of online handwriting. In: 2018 10th international congress on ultra modern telecommunications and control systems and workshops (ICUMT). IEEE, pp 1–6. https://doi.org/10.1109/ICUMT.2018.8631265

  15. 15.

    Paragliola G, Coronato A (2018) Gait anomaly detection of subjects with Parkinson’s disease using a deep time series-based approach. IEEE Access 6:73280–73292. https://doi.org/10.1109/ACCESS.2018.2882245

    Article  Google Scholar 

  16. 16.

    Diaz M, Ferrer MA, Impedovo D, Pirlo G, Vessio G (2019) Dynamically enhanced static handwriting representation for Parkinson’s disease detection. Pattern Recogn Lett 128:204–210. https://doi.org/10.1016/j.patrec.2019.08.018

    Article  Google Scholar 

  17. 17.

    Senatore R, Della Cioppa A, Marcelli A (2019) Automatic diagnosis of Parkinson disease through handwriting analysis: a Cartesian genetic programming approach. In: 2019 IEEE 32nd international symposium on computer-based medical systems (CBMS). IEEE, pp 312–317. https://doi.org/10.1109/CBMS.2019.00071

  18. 18.

    Rios-Urrego CD, Vásquez-Correa JC, Vargas-Bonilla JF, Nöth E, Lopera F, Orozco-Arroyave JR (2019) Analysis and evaluation of handwriting in patients with Parkinson’s disease using kinematic, geometrical, and non-linear features. Comput Methods Progr Biomed 173:43–52. https://doi.org/10.1016/j.cmpb.2019.03.005

    Article  Google Scholar 

  19. 19.

    Gupta JD, Chanda B (2019) Novel features for diagnosis of Parkinson’s disease from off-line archimedean spiral images. In: 2019 IEEE 10th international conference on awareness science and technology (iCAST). IEEE, pp 1–6. https://doi.org/10.1109/ICAwST.2019.8923159

  20. 20.

    Aouraghe I, Ammour A, Khaissidi G, Mrabti M, Aboulem G, Belahsen F (2019) Automatic analysis of arabic online handwriting of patients with Parkinson's disease: statistical study and classification. In: Proceedings of the new challenges in data sciences: acts of the second conference of the Moroccan Classification Society, vol 24, pp 1–5. https://doi.org/10.1145/3314074.3314100

  21. 21.

    Gupta U, Bansal H, Joshi D (2020) An improved sex-specific and age-dependent classification model for Parkinson’s diagnosis using handwriting measurement. Comput Methods Programs Biomed 189:105305. https://doi.org/10.1016/j.cmpb.2019.105305

    Article  Google Scholar 

  22. 22.

    Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2020) Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 32(3):839–854. https://doi.org/10.1007/s00521-019-04069-0

    Article  Google Scholar 

  23. 23.

    Aouraghe I, Alae A, Ghizlane K, Mrabti M, Aboulem G, Faouzi B (2020) A novel approach combining temporal and spectral features of Arabic online handwriting for Parkinson’s disease prediction. J Neurosci Methods. https://doi.org/10.1016/j.jneumeth.2020.108727

    Article  Google Scholar 

  24. 24.

    Alaskar H, Hussain AJ, Khan W, Tawfik H, Trevorrow P, Liatsis P, Sbaï Z (2020) A data science approach for reliable classification of neuro-degenerative diseases using gait patterns. J Reliab Intell Environ 6(4):233–247. https://doi.org/10.1007/s40860-020-00114-1

    Article  Google Scholar 

  25. 25.

    “UCI Machine Learning Repository: Parkinsons Data Set”. https://archive.ics.uci.edu/ml/datasets/Parkinson+Disease+Spiral+Drawings+Using+Digitized+Graphics+Tablet. Accessed 04 September 2020

  26. 26.

    Remeseiro B, Bolon-Canedo V (2019) A review of feature selection methods in medical applications. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2019.103375

    Article  Google Scholar 

  27. 27.

    Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024

    Article  Google Scholar 

  28. 28.

    Rani P, Kumar R, Jain A, Lamba R (2020) Taxonomy of machine learning algorithms and its applications. J Comput Theror Nanosci 17(6):2509–2514. https://doi.org/10.1166/jctn.2020.8922

    Article  Google Scholar 

  29. 29.

    Rani P, Kumar R, Jain A (2020) Multistage model for accurate prediction of missing values in heart disease dataset. In: Proceedings of international conference on sentimental analysis and deep learning, pp 147–158

  30. 30.

    Tama BA, Im S, Lee S (2020) Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble. Biomed Res Int. https://doi.org/10.1155/2020/9816142

    Article  Google Scholar 

Download references


Not applicable.

Author information



Corresponding author

Correspondence to Rohit Lamba.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Availability of data and material

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lamba, R., Gulati, T., Al-Dhlan, K.A. et al. A systematic approach to diagnose Parkinson’s disease through kinematic features extracted from handwritten drawings. J Reliable Intell Environ (2021). https://doi.org/10.1007/s40860-021-00130-9

Download citation


  • Parkinson’s disease
  • Decision support system
  • Kinematic features
  • Feature selection
  • Classifier algorithm
  • Machine learning