Clustering of Data that Quantify the Degree of Impairment of the Upper Limb in Patients with Alterations of the Central Nervous System

Leonardo Anaya, Ivett Quinones, Yannick Quijano, Virginia Bueyes, Enrique Chong, Victor Ponce

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

1 Scopus citations

Abstract

Previous studies have considered improving the classification procedures for motor impairment of the upper limb in patients with Central Nervous System alterations. This work compares two classification methods to be able to group the SSULF scale into five classes to have a better assessment, results showed that with the K-Means more than the 95% of the control group SALM values were correctly classified in SSULF 1 and with the Fuzzy C-means the 92%, so we can assume that the K-means method did a better classification for our purpose.

Original languageEnglish
Title of host publication2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189871
DOIs
StatePublished - 11 Nov 2020
Event17th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2020 - Virtual, Mexico City, Mexico
Duration: 11 Nov 202013 Nov 2020

Publication series

Name2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2020

Conference

Conference17th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2020
Country/TerritoryMexico
CityVirtual, Mexico City
Period11/11/2013/11/20

Keywords

  • clustering (unsupervised) algorithm
  • data classification
  • smoothness of movement

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