An evaluation of clustering techniques to classify dexterous manipulation of individuals with and without dysfunction

Emily L. Lawrence, Isabella Fassola, Sudarshan Dayanidhi, Caroline Leclercq, Francisco J. Valero-Cuevas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The rehabilitation of manipulation ability in orthopedic (e.g., thumb carpometacarpal osteoarthritis-CMC OA) and neurological (e.g., Parkinson's disease-PD) conditions depends critically on our ability to detect dysfunction and quantify its evolution and response to treatment. The Strength-Dexterity (SD) test is a validated indicator of dynamic dexterous manipulation function, but its ability to categorize clinical populations has not been tested. We 1) used the SD test to compare manipulation ability among patients with OA and PD and healthy age-matched elderly control subjects; and 2) compared and evaluated the ability of different clustering techniques to classify subjects into clinical or control groups and calculate their respective cluster centroids. We considered five clustering methods (three hard and two fuzzy): K-means, K-medoids, Gaussian expectation-maximization (GEM), Subtractive, and Fuzzy C-means clustering. We found the centroids of the SD test scores differed significantly between the clinical and control groups. Of the five methods considered, the GEM clustering algorithm most accurately classified SD test performance between these two groups.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages1254-1257
Number of pages4
DOIs
StatePublished - 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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