Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson's disease patients

Alejandro Garza-Rodríguez, Luis Pastor Sánchez-Fernández, Luis Alejandro Sánchez-Pérez, José Juan Carbajal Hernández

Research output: Contribution to journalArticlepeer-review


Nowadays, the Unified Parkinson Disease Rating Scale supported by the Movement Disorder Society (MDS-UPDRS), is a standardized and widely accepted instrument to rate Parkinson's disease (PD). This work presents a thorough analysis of item 3.6 of the MDS-UPDRS scale which corresponds to the pronation and supination hand movements. The motivation for this work lies in the objective quantification of motor affectations not covered by the MDS-UPDRS scale such as unsteady oscillations and velocity decrements during the motor exploration. Overall, 12 different bio-mechanical features were quantified based on measurements performed by inertial measurement units (IMUs). After a feature selection process, the selected bio-mechanical features were used as inputs for a fuzzy inference model that predicts the stage of development of the disease in each patient. In addition to this model's output, the scores of three different expert examiners and the output of a fuzzy inference model which covers affectations strictly attached the MDS-UPDRS guidelines, were also considered to obtain an integrated computational model. The proposed integrated model was incorporated using the Analytic Hierarchy Process (AHP), which gives the novelty of a combined score that helps expert examiners to give a broader assessment of the disease that covers both affectations mentioned in the MDS-UPDRS guidelines and affectations not covered by it in an objective manner.

Original languageEnglish
Article number101873
JournalArtificial Intelligence in Medicine
StatePublished - May 2020


  • Feature extraction
  • Fuzzy inference
  • Parkinson
  • Pronation supination

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