Human gait classification after lower limb fracture using Artificial Neural Networks and principal component analysis

Vertical ground reaction force (vGRF) has been commonly used in human gait analysis making possible the study of mechanical overloads in the locomotor system. This study aimed at applying the principal component (PC) analysis and two Artificial Neural Networks (ANN), multi-layer feed forward (FF) and self organized maps (SOM), for classifying and clustering gait patterns from normal subjects (CG) and patients with lower limb fractures (FG). The vGRF from a group of 51 subjects, including 38 in CG and 13 in FG were used for PC analysis and classification. It was also tested the classification of vGRF from five subjects in a treatment group (TG) that were submitted to a physiotherapeutic treatment. Better results were obtained using four PC as inputs of the ANN, with 96% accuracy, 100% specificity and 85% sensitivity using SOM, against 92% accuracy, 100% specificity and 69% sensitivity for FF classification. After treatment, three of five subjects were classified as presenting normal vGRF.

[1]  Marimuthu Palaniswami,et al.  Support vector machines for automated gait classification , 2005, IEEE Transactions on Biomedical Engineering.

[2]  P. Allard,et al.  Main functional roles of knee flexors/extensors in able-bodied gait using principal component analysis (I). , 2002, The Knee.

[3]  Jurandir Nadal,et al.  Principal Component Analysis of Vertical Ground Reaction Force: A Powerful Method to Discriminate Normal and Abnormal Gait and Assess Treatment , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Andreas Daffertshofer,et al.  PCA in studying coordination and variability: a tutorial. , 2004, Clinical biomechanics.

[5]  F F Nobre,et al.  Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait. , 2010, Journal of biomechanics.

[6]  Joarder Kamruzzaman,et al.  A comparison of neural networks and support vector machines for recognizing young-old gait patterns , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[7]  K. Lyons,et al.  Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network , 2008, Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  J. Nadal,et al.  Application of principal component analysis in vertical ground reaction force to discriminate normal and abnormal gait. , 2009, Gait & posture.

[9]  Dieter Merkl,et al.  Clinical gait analysis by neural networks: issues and experiences , 1997, Proceedings of Computer Based Medical Systems.