Annals of Biomedical Engineering

, Volume 46, Issue 12, pp 2057–2068 | Cite as

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

  • Erika Rovini
  • Carlo Maremmani
  • Alessandra Moschetti
  • Dario Esposito
  • Filippo CavalloEmail author


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.


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



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

Supplementary material

10439_2018_2104_MOESM1_ESM.docx (24 kb)
Supplementary material 1 (DOCX 23 kb)
10439_2018_2104_MOESM2_ESM.pdf (746 kb)
Supplementary material 2 (PDF 748 kb)


  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.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.CrossRefGoogle 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. 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.CrossRefGoogle 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. 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.CrossRefGoogle Scholar
  8. 8.
    Breiman, L. Random Forest. Mach. Learn. 45:5–32, 2001.CrossRefGoogle Scholar
  9. 9.
    Burges, C. J. C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2:121–167, 1998.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  17. 17.
    Hoehn, M. M., and M. D. Yahr. Parkinsonism: onset, progression, and mortality. Neurology 17:427–442, 1967.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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. 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  24. 24.
    Palma, J.-A., and H. Kaufmann. Autonomic disorders predicting Parkinson disease. Parkinsonism Relat. Disord. 20:S94–S98, 2014.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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. 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.CrossRefGoogle Scholar
  35. 35.
  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.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2018

Authors and Affiliations

  1. 1.The BioRobotics InstituteScuola Superiore Sant’AnnaPontederaItaly
  2. 2.U.O. Neurologia, Ospedale Delle Apuane (AUSL Toscana Nord Ovest)MassaItaly

Personalised recommendations