Unsupervised Learning from Motion Sensor Data to Assess the Condition of Patients with Parkinson’s Disease

  • Teodora Matić
  • Somayeh Aghanavesi
  • Mevludin Memedi
  • Dag Nyholm
  • Filip Bergquist
  • Vida Groznik
  • Jure Žabkar
  • Aleksander SadikovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)


Parkinson’s disease (PD) is a chronic neurodegenerative disorder that predominantly affects the patient’s motor system, resulting in muscle rigidity, bradykinesia, tremor, and postural instability. As the disease slowly progresses, the symptoms worsen, and regular monitoring is required to adjust the treatment accordingly. The objective evaluation of the patient’s condition is sometimes rather difficult and automated systems based on various sensors could be helpful to the physicians. The data in this paper come from a clinical study of 19 advanced PD patients with motor fluctuations. The measurements used come from the motion sensors the patients wore during the study. The paper presents an unsupervised learning approach applied on this data with the aim of checking whether sensor data alone can indicate the patient’s motor state. The rationale for the unsupervised approach is that there was significant inter-physician disagreement on the patient’s condition (target value for supervised machine learning). The input to clustering came from sensor data alone. The resulting clusters were matched against the physicians’ estimates showing relatively good agreement.


Unsupervised learning Motion sensor Parkinson’s disease Objective evaluation Patient monitoring Bradykinesia Dyskinesia 



The research is supported by the Slovenian Research Agency (ARRS) under the Artificial Intelligence and Intelligent Systems Programme (ARRS No. P2-0209), Swedish Knowledge Foundation, and Swedish Agency for Innovation.


  1. 1.
    Aghanavesi, S., Filip, B., Nyholm, D., Senek, M., Memedi, M.: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms. In: International Congress of Parkinson’s Disease and Movement Disorders (MDS), Hong Kong, 5–9 October 2018 (2018)Google Scholar
  2. 2.
    Demšar, J., Leban, G., Zupan, B.: Freeviz–an intelligent multivariate visualization approach to explorative analysis of biomedical data. J. Biomed. Inform. 40(6), 661–671 (2007)CrossRefGoogle Scholar
  3. 3.
    Fritz, H., Garcıa-Escudero, L.A., Mayo-Iscar, A.: tclust: an R package for a trimming approach to cluster analysis. J. Stat. Softw. 47(12), 1–26 (2012)CrossRefGoogle Scholar
  4. 4.
    Nyholm, D., et al.: Duodenal levodopa infusion monotherapy vs oral polypharmacy in advanced parkinson disease. Neurology 64(2), 216–223 (2005). Scholar
  5. 5.
    Senek, M., et al.: Levodopa/carbidopa microtablets in Parkinson’s disease: a study of pharmacokinetics and blinded motor assessment. Eur. J. Clin. Pharmacol. 73(5), 563–571 (2017). Scholar
  6. 6.
    Thomas, I., et al.: A treatment-response index from wearable sensors for quantifying Parkinson’s disease motor states. IEEE J. Biomed. Health Inform. 22(5), 1341–1349 (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Teodora Matić
    • 1
  • Somayeh Aghanavesi
    • 2
  • Mevludin Memedi
    • 3
  • Dag Nyholm
    • 4
  • Filip Bergquist
    • 5
  • Vida Groznik
    • 1
    • 6
  • Jure Žabkar
    • 1
  • Aleksander Sadikov
    • 1
    Email author
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Computer Engineering, School of Technology and Business StudiesDalarna UniversityDalarnaSweden
  3. 3.Informatics, Business SchoolÖrebro UniversityÖrebroSweden
  4. 4.Department of Neuroscience, NeurologyUppsala UniversityUppsalaSweden
  5. 5.Department of PharmacologyUniversity of GothenburgGothenburgSweden
  6. 6.Faculty of Mathematics, Natural Sciences and Information TechnologiesUniversity of PrimorskaKoperSlovenia

Personalised recommendations