FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2524932991> ?p ?o ?g. }

• W2524932991 endingPage "278" @default.
• W2524932991 startingPage "268" @default.
• W2524932991 abstract "Gait analysis plays an important role in maintaining the well-being of human mobility and health care, and is a valuable tool for obtaining quantitative information on motor deficits in Parkinson's disease (PD). In this paper, we propose a method to classify (diagnose) patients with PD and healthy control subjects using gait analysis via deterministic learning theory. The classification approach consists of two phases: a training phase and a classification phase. In the training phase, gait characteristics represented by the gait dynamics are derived from the vertical ground reaction forces under the usual and self-selected paces of the subjects. The gait dynamics underlying gait patterns of healthy controls and PD patients are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. The gait patterns of healthy controls and PD patients constitute a training set. In the classification phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern of a certain PD patient to be classified (diagnosed), a set of classification errors are generated. The average L1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test PD gait pattern according to the smallest error principle. When the gait patterns of 93 PD patients and 73 healthy controls are classified with five-fold cross-validation method, the accuracy, sensitivity and specificity of the results are 96.39%, 96.77% and 95.89%, respectively. Based on the results, it may be claimed that the features and the classifiers used in the present study could effectively separate the gait patterns between the groups of PD patients and healthy controls." @default.
• W2524932991 created "2016-10-07" @default.
• W2524932991 creator A5002495815 @default.
• W2524932991 creator A5004219097 @default.
• W2524932991 creator A5016393389 @default.
• W2524932991 creator A5018863416 @default.
• W2524932991 creator A5022209006 @default.
• W2524932991 creator A5071773009 @default.
• W2524932991 date "2016-10-01" @default.
• W2524932991 modified "2023-10-16" @default.
• W2524932991 title "Parkinson's disease classification using gait analysis via deterministic learning" @default.
• W2524932991 cites W1498383300 @default.
• W2524932991 cites W1983467873 @default.
• W2524932991 cites W1988981848 @default.
• W2524932991 cites W1994847376 @default.
• W2524932991 cites W1996101704 @default.
• W2524932991 cites W1996769879 @default.
• W2524932991 cites W2005490390 @default.
• W2524932991 cites W2006199542 @default.
• W2524932991 cites W2008151608 @default.
• W2524932991 cites W2009398857 @default.
• W2524932991 cites W2013808389 @default.
• W2524932991 cites W2014503804 @default.
• W2524932991 cites W2021278861 @default.
• W2524932991 cites W2035768376 @default.
• W2524932991 cites W2040162990 @default.
• W2524932991 cites W2047706369 @default.
• W2524932991 cites W2057423886 @default.
• W2524932991 cites W2058190976 @default.
• W2524932991 cites W2059110392 @default.
• W2524932991 cites W2069049175 @default.
• W2524932991 cites W2080508823 @default.
• W2524932991 cites W2089744082 @default.
• W2524932991 cites W2098882356 @default.
• W2524932991 cites W2102426826 @default.
• W2524932991 cites W2102670529 @default.
• W2524932991 cites W2113442785 @default.
• W2524932991 cites W2138443306 @default.
• W2524932991 cites W2145954079 @default.
• W2524932991 cites W2153761521 @default.
• W2524932991 cites W2154103615 @default.
• W2524932991 cites W2160588587 @default.
• W2524932991 cites W2164540657 @default.
• W2524932991 cites W2165674060 @default.
• W2524932991 cites W2172270388 @default.
• W2524932991 cites W2405341132 @default.
• W2524932991 doi "https://doi.org/10.1016/j.neulet.2016.09.043" @default.
• W2524932991 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27693437" @default.
• W2524932991 hasPublicationYear "2016" @default.
• W2524932991 type Work @default.
• W2524932991 sameAs 2524932991 @default.
• W2524932991 citedByCount "81" @default.
• W2524932991 countsByYear W25249329912017 @default.
• W2524932991 countsByYear W25249329912018 @default.
• W2524932991 countsByYear W25249329912019 @default.
• W2524932991 countsByYear W25249329912020 @default.
• W2524932991 countsByYear W25249329912021 @default.
• W2524932991 countsByYear W25249329912022 @default.
• W2524932991 countsByYear W25249329912023 @default.
• W2524932991 crossrefType "journal-article" @default.
• W2524932991 hasAuthorship W2524932991A5002495815 @default.
• W2524932991 hasAuthorship W2524932991A5004219097 @default.
• W2524932991 hasAuthorship W2524932991A5016393389 @default.
• W2524932991 hasAuthorship W2524932991A5018863416 @default.
• W2524932991 hasAuthorship W2524932991A5022209006 @default.
• W2524932991 hasAuthorship W2524932991A5071773009 @default.
• W2524932991 hasConcept C105795698 @default.
• W2524932991 hasConcept C151800584 @default.
• W2524932991 hasConcept C153180895 @default.
• W2524932991 hasConcept C154945302 @default.
• W2524932991 hasConcept C173906292 @default.
• W2524932991 hasConcept C177264268 @default.
• W2524932991 hasConcept C185429906 @default.
• W2524932991 hasConcept C199360897 @default.
• W2524932991 hasConcept C33923547 @default.
• W2524932991 hasConcept C41008148 @default.
• W2524932991 hasConcept C50644808 @default.
• W2524932991 hasConcept C71924100 @default.
• W2524932991 hasConcept C99508421 @default.
• W2524932991 hasConceptScore W2524932991C105795698 @default.
• W2524932991 hasConceptScore W2524932991C151800584 @default.
• W2524932991 hasConceptScore W2524932991C153180895 @default.
• W2524932991 hasConceptScore W2524932991C154945302 @default.
• W2524932991 hasConceptScore W2524932991C173906292 @default.
• W2524932991 hasConceptScore W2524932991C177264268 @default.
• W2524932991 hasConceptScore W2524932991C185429906 @default.
• W2524932991 hasConceptScore W2524932991C199360897 @default.
• W2524932991 hasConceptScore W2524932991C33923547 @default.
• W2524932991 hasConceptScore W2524932991C41008148 @default.
• W2524932991 hasConceptScore W2524932991C50644808 @default.
• W2524932991 hasConceptScore W2524932991C71924100 @default.
• W2524932991 hasConceptScore W2524932991C99508421 @default.
• W2524932991 hasFunder F4320321001 @default.
• W2524932991 hasLocation W25249329911 @default.
• W2524932991 hasLocation W25249329912 @default.
• W2524932991 hasOpenAccess W2524932991 @default.
• W2524932991 hasPrimaryLocation W25249329911 @default.
• W2524932991 hasRelatedWork W1954924192 @default.

• next