Application of Kohonen maps to kinetic analysis of human gait

Abstract

In recent years the use of artificial neural networks for classification and analysis of kinematic and kinetic characteristics of human locomotion has greatly increased. This happens in an attempt to overcome the limitations of traditional dynamic analysis and to find new clinical indicators for interpreting quick and objectively the large amount of information obtained in a gait lab. One of the most widely used neural networks for human gait analysis is the self-organizing or Kohonen map, based on unsupervised learning without prior definition of the formed natural groups. Among the advantages of using this type of neural network is the data dimensionality reduction, with minimal loss of information content, and the grouping of them in function of their similarities. Taking into account this, in this work an application case of a Kohonen map for clustering of locomotion kinetic characteristics in normal and Parkinson's disease individuals is presented. The results indicate that the groups identified by the map are consistent with the classification carried out by experts in function of traditional gait dynamic analysis, showing the potential of this technique for distinguishing between a population of individuals with normal gait and with gait disorders of different etiology.Keywords Human gait, Parkinson´s disease, Artificial neural network, Clustering. Aplicação de mapas de Kohonen à análise cinética da marcha humana Resumo Nos últimos anos, tem aumentado significativamente o uso de redes neurais artificiais para a classificação e análise cinemática e cinética da marcha humana, em uma tentativa de superar as limitações da análise dinâmica tradicional e de encontrar novos indicadores clínicos para interpretar

Kohonen Neural Network Investigation of the Effects of the Visual, Proprioceptive and Vestibular Systems to Balance in Young Healthy Adult Subjects

“…The results showed that the balance coordination pattern between pre- and post-tests for the slackline task was significantly different [ 23 ]. Gait investigation of the data from 60 healthy normal subjects (mean age 63.3 years, age range 37 to 86 years, 47% men) and 60 patients (mean age 68.8 years, age range 45 to 84 years, 62% of men) with idiopathic Parkinson disease using Kohonen network showed that the identified groupings were consistent with the classification carried out by experts in function of traditional gait dynamic analysis [ 24 ]. Kohonen network has also been applied in areas of balance to identify movement patterns and variability among young and older adults [ 25 ].…”

“…The operation of the network as described in [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ] is summarized using the following 4 steps: Initialization …”

Use of computational intelligence in the genetic divergence of colored cotton plants

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