A Novel Approach for Movement Evolution Tracking in Parkinson's Disease using Data Analysis and Fuzzy Logic

David Martin*, Mirela Popa, Jennifer Jiménez, Federico Álvarez, Stylianos Asteriadis, Laura Carrasco

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


In this paper, a novel approach for the analysis of the movement evolution in patients with Parkinson's disease is presented. The system offers the capabilities of detecting significant degradations in the motor-skills of the patients according to the physiotherapy evaluations, where seven items are measured, including: posture, balance, walking, postural changes, involuntary movements, movement coordination and rigidity. To assess their evolution, two modules are employed: a data analysis module, which uses a clustering algorithm to distribute patients according to their skills and analyses their evolution based on the last three evaluations, and a Decision Support Tool based on a Fuzzy-Logic system, which measures the state of the patient according to the results from the mentioned data analysis module and generates a report per patient including his/her state at each item as well as recommendations considering convenient exercises to be practiced. Thus, the system provides meaningful information to physiotherapists, to support them in the decision-making process.
Original languageEnglish
Title of host publicationProceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference
PublisherThe Association for Computing Machinery
Number of pages7
ISBN (Print)9781450363907
Publication statusPublished - 2018
Event11th ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA) - GREECE
Duration: 26 Jun 201829 Jun 2018


Conference11th ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA)


  • Decision support tool
  • fuzzy-logic system
  • data analysis
  • clustering algorithms
  • Parkinson's disease
  • physiotherapy recommendations
  • motor-skills evolution

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