Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches


Millions of people worldwide are affected by Parkinson’s disease (PD), which significantly worsens their quality of life. Currently, the diagnosis is based on assessment of motor symptoms, but interest toward non-motor symptoms is increasing, as well. Among them, idiopathic hyposmia (IH) is associated with an increased risk of developing PD in healthy adults. In this work, a wearable inertial device, named SensFoot V2, was used to acquire motor data from 30 healthy subjects, 30 people with IH, and 30 PD patients while performing tasks from the MDS-UPDRS III for lower limb assessment. The most significant and non-correlated extracted parameters were selected in a feature array that can identify differences between the three groups of people. A comparative classification analysis was performed by applying three supervised machine learning algorithms. The system resulted able to distinguish between healthy and patients (specificity and recall equal to 0.967), and the people with IH can be identified as a separate class within a three-group classification (accuracy equal to 0.78). Thus, the system could support the clinician in objective assessment of PD. Further, identification of IH together with changes in motor parameters could be a non-invasive two-step approach to investigate the early onset of PD.

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

Figure 1
Figure 2
Figure 3
Figure 4


  1. 1.

    Al-Aidaroos, K. M., A. Abu Bakar, and Z. Othman. Naïve Bayes variants in classification learning. Proc.2010 Int. Conf. Inf. Retr. Knowl. Manag. Explor. Invis. World, CAMP’10 276–281, 2010.

  2. 2.

    Alam, M. N., A. Garg, T. T. K. Munia, R. Fazel-Rezai, and K. Tavakolian. Vertical ground reaction force marker for Parkinson’s disease. PLoS ONE 12:1–13, 2017.

    CAS  Google Scholar 

  3. 3.

    Antonini, A., R. Benti, S. De Notaris, S. Tesei, A. Zecchinelli, G. Sacilotto, N. Meucci, M. Canesi, C. Mariani, G. Pezzoli, et al. 123I-Ioflupane/SPECT binding to striatal dopamine transporter (DAT) uptake in patients with Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy. Neurol. Sci. 24:149–150, 2003.

    Article  CAS  PubMed  Google Scholar 

  4. 4.

    Arora, S., V. Venkataraman, S. Donohue, K. M. Biglan, E. R. Dorsey, and M. A. Little. High accuracy discrimination of Parkinson’s disease participants from healthy controls using smartphones. Acoust. Speech Signal Process. 2014.

    Article  Google Scholar 

  5. 5.

    Atkinson-Clement, C., S. Pinto, A. Eusebio, and O. Coulon. Diffusion tensor imaging in Parkinson’s disease: review and meta-analysis. NeuroImage Clin. 16:98–110, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Barth, J., J. Klucken, P. Kugler, T. Kammerer, R. Steidl, J. Winkler, J. Hornegger, and B. Eskofier. Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson’s Disease. Eng. Med. Biol. Soc. 2011.

    Article  Google Scholar 

  7. 7.

    Berg, D., A. E. Lang, R. B. Postuma, W. Maetzler, G. Deuschl, T. Gasser, A. Siderowf, A. H. Schapira, W. Oertel, J. A. Obeso, C. W. Olanow, W. Poewe, and M. Stern. Changing the research criteria for the diagnosis of Parkinson’s disease: obstacles and opportunities. Lancet Neurol. 12:514–524, 2013.

    Article  PubMed  Google Scholar 

  8. 8.

    Breiman, L. Random Forest. Mach. Learn. 45:5–32, 2001.

    Article  Google Scholar 

  9. 9.

    Burges, C. J. C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2:121–167, 1998.

    Article  Google Scholar 

  10. 10.

    Dorsey, E. R., R. Constantinescu, J. P. Thompson, K. M. Biglan, R. G. Holloway, K. Kieburtz, F. J. Marshall, B. M. Ravina, G. Schifitto, A. Siderowf, and C. M. Tanner. Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68(5):384–386, 2007.

    Article  CAS  Google Scholar 

  11. 11.

    Fahn, S. Clinical aspects of Parkinson disease. In: Parkinson’s disease: molecular and therapeutic insights from model systems, edited by R. Nass, and S. Przedborski. Amsterdam: Elsevier Inc., 2008, pp. 3–48.

    Google Scholar 

  12. 12.

    Galantucci, S., F. Agosta, E. Stefanova, S. Basaia, M. P. Van Den Heuvel, T. Stojković, E. Canu, I. Stanković, V. Spica, M. Copetti, et al. Structural brain connectome and cognitive impairment in Parkinson disease. Radiology 283:515–525, 2016.

    Article  PubMed  Google Scholar 

  13. 13.

    Gelb, D. J., E. Oliver, and S. Gilman. Criteria for the diagnosis of Parkinson’s Disease. Arch. Neurol. 56:33–39, 1999.

    Article  CAS  PubMed  Google Scholar 

  14. 14.

    Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. Random forests for land cover classification. Pattern Recognit. Lett. 27:294–300, 2006.

    Article  Google Scholar 

  15. 15.

    Goetz, C. G., B. C. Tilley, S. R. Shaftman, G. T. Stebbins, S. Fahn, P. Martinez-Martin, W. Poewe, C. Sampaio, M. Stern, R. Dodel, B. Dubois, R. G. Holloway, J. Jankovic, J. Kulisevsky, A. E. Lang, A. J. Lees, S. Leurgans, P. A. LeWitt, D. Nyenhuis, C. W. Olanow, O. Rascol, A. Schrag, J. A. Teresi, J. J. van Hilten, and N. LaPelle. Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23:2129–2170, 2008.

    Article  PubMed  Google Scholar 

  16. 16.

    Haugen, J., M. L. T. M. Muller, V. Kotagal, R. L. Albin, R. A. Koeppe, P. J. H. Scott, K. A. Frey, and N. I. Bohnen. Prevalence of impaired odor identification in Parkinson disease with imaging evidence of nigrostriatal denervation. J. Neural Transm. 123:421–424, 2016.

    Article  CAS  PubMed  Google Scholar 

  17. 17.

    Hoehn, M. M., and M. D. Yahr. Parkinsonism: onset, progression, and mortality. Neurology 17:427–442, 1967.

    Article  CAS  Google Scholar 

  18. 18.

    Khorasani, A., and M. R. Daliri. HMM for Classification of Parkinson’s Disease based on the raw gait data. J. Med. Syst. 38:147, 2014.

    Article  PubMed  Google Scholar 

  19. 19.

    Kim, J., B. S. Kim, and S. Savarese. Comparing image classification methods: K-nearest-neighbor and support-vector-machines. Stevens Point: World Scientific and Engineering Academy and Society (WSEAS), pp. 133–138, 2012.

    Google Scholar 

  20. 20.

    Kim, J.-W., Y. Kwon, Y.-M. Kim, H.-Y. Chung, G.-M. Eom, J.-H. Jun, J.-W. Lee, S.-B. Koh, B. K. Park, and D.-K. Kwon. Analysis of lower limb bradykinesia in Parkinson’s disease patients. Geriatr. Gerontol. Int. 12:257–264, 2012.

    Article  PubMed  Google Scholar 

  21. 21.

    Kugler, P., and C. Jaremenko. Automatic recognition of Parkinson’s disease using surface electromyography during standardized gait tests. Eng. Med. Biol. Soc. 2013.

    Article  Google Scholar 

  22. 22.

    Maremmani, C., F. Cavallo, C. Purcaro, G. Rossi, S. Salvadori, E. Rovini, D. Esposito, A. Pieroni, S. Ramat, P. Vanni, B. Fattori, and G. Meco. Combining olfactory test and motion analysis sensors in Parkinson’s disease preclinical diagnosis: a pilot study. Acta Neurol. Scand. 137:204–211, 2018.

    Article  CAS  PubMed  Google Scholar 

  23. 23.

    Maremmani, C., G. Rossi, N. Tambasco, B. Fattori, A. Pieroni, S. Ramat, A. Napolitano, P. Vanni, P. Serra, P. Piersanti, M. Zanetti, M. Coltelli, M. Orsini, R. Marconi, C. Purcaro, A. Rossi, P. Calabresi, and G. Meco. The validity and reliability of the Italian olfactory identification test (IOIT) in healthy subjects and in Parkinson’s disease patients. Parkinsonism Relat. Disord. 18:788–793, 2012.

    Article  PubMed  Google Scholar 

  24. 24.

    Palma, J.-A., and H. Kaufmann. Autonomic disorders predicting Parkinson disease. Parkinsonism Relat. Disord. 20:S94–S98, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Palmerini, L., S. Mellone, G. Avanzolini, F. Valzania, and L. Chiari. Quantification of motor impairment in Parkinson’s disease using an instrumented timed up and go test. IEEE Trans. Neural Syst. Rehabil. Eng. 21:664–673, 2013.

    Article  PubMed  Google Scholar 

  26. 26.

    Patel, S., K. Lorincz, R. Hughes, N. Huggins, J. Growdon, D. Standaert, M. Akay, J. Dy, M. Welsh, and P. Bonato. Monitoring motor fluctuations in patients with Parkinson’s disease using wearable sensors. IEEE Trans. Inf. Technol. Biomed. 13:864–873, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Perumal, S. V., and R. Sankar. Gait and tremor assessment for patients with Parkinson’s disease using wearable sensors. ICT Express 2:168–174, 2016.

    Article  Google Scholar 

  28. 28.

    Ponsen, M. M., D. Stoffers, E. C. Wolters, J. Booij, and H. W. Berendse. Olfactory testing combined with dopamine transporter imaging as a method to detect prodromal Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 81:396–399, 2010.

    Article  PubMed  Google Scholar 

  29. 29.

    Rigas, G., A. T. Tzallas, M. G. Tsipouras, P. Bougia, E. E. Tripoliti, D. Baga, D. I. Fotiadis, S. G. Tsouli, and S. Konitsiotis. Assessment of tremor activity in the Parkinson’s Disease using a set of wearable sensors. IEEE Trans. Inf. Technol. Biomed. 16:478–487, 2012.

    Article  PubMed  Google Scholar 

  30. 30.

    Rovini, E., C. Maremmani, and F. Cavallo. How wearable sensors can support Parkinson’s disease diagnosis and treatment: a systematic review. Front. Neurosci. 11:555, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Sarkar, S., J. Raymick, and S. Imam. Neuroprotective and therapeutic strategies against Parkinson’s disease: recent perspectives. Int. J. Mol. Sci. 17(6):904, 2016.

    Article  CAS  PubMed Central  Google Scholar 

  32. 32.

    Schapira, A. H., R. K. Chauduri, and P. Jenner. Non-motor features of Parkinson disease. Nat. Rev. Neurosci. 18:435–450, 2017.

    Article  CAS  PubMed  Google Scholar 

  33. 33.

    Tien, I., S. D. Glaser, and M. J. Aminoff. Characterization of gait abnormalities in Parkinson’s disease using a wireless inertial sensor system. Eng. Med. Biol. Soc. 2010.

    Article  Google Scholar 

  34. 34.

    Wahid, F., R. K. Begg, C. J. Hass, S. Halgamuge, and D. C. Ackland. Classification of Parkinson’s disease gait using spatial-temporal gait features. IEEE J. Biomed. Health Inform. 19:1794–1802, 2015.

    Article  PubMed  Google Scholar 

  35. 35.

    Yan, K. YAN-PRTools., 2016.

  36. 36.

    Yang, K., W.-X. Xiong, F.-T. Liu, Y.-M. Sun, S. Luo, Z.-T. Ding, J.-J. Wu, and J. Wang. Objective and quantitative assessment of motor function in Parkinson’s disease—from the perspective of practical applications. Ann. Transl. Med. 4:90, 2016.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


This work was financially supported by DAPHNE project (Regione Toscana PAR FAS 2007-2013, Bando FAS SALUTE 2014, CUP J52I16000170002).

Author information



Corresponding author

Correspondence to Filippo Cavallo.

Additional information

Associate Editor Andreas Anayiotos oversaw the review of this article.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 23 kb)

Supplementary material 2 (PDF 748 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rovini, E., Maremmani, C., Moschetti, A. et al. Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches. Ann Biomed Eng 46, 2057–2068 (2018).

Download citation


  • Decision support systems
  • Idiopathic hyposmia
  • Inertial wearable sensors
  • Motion analysis
  • Supervised learning