Determination of input variables for the development of a gait asymmetry expert system in patients with idiopathic scoliosis

The purpose of this study was to select the appropriate input variables for the development of an expert system to analyze the gait asymmetry of patients with idiopathic scoliosis. Gait experiments were performed with 12 healthy female adolescents and 16 female adolescents with untreated adolescent idiopathic scoliosis. The experimental equipment included six infrared cameras and two ground reaction force platforms. By using a 3D human model, gait elements, kinematic and kinetic data were extracted. Self-organizing map and genetic algorithm were used for proper selection of input variables, and these methods were validated by using auto regression models, which were described in previous studies. Sixty gait variables based on a literature review were selected, and Self-organizing map was used to maintain the independency between the input variables, and the 39 independent retaining variables were chosen. Also, in order to identify the inputs exhibiting a significant relationship with the output, a genetic algorithm-general regression neural network was applied; and the frequency of the solution set was measured by genetic algorithm iteration. A stepwise method was applied based on the variables with high frequency, and final 11 input variables were selected. Furthermore, a back propagation artificial neural network with high accuracy 96.3(3.2)%, which can discriminate patients from the normal subjects, was developed with selected 11 input variables. Therefore, the results of this study can be used as input variables for the development of a gait asymmetry expert system.

[1]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river , 2005 .

[2]  R. Winter,et al.  The selection of fusion levels in thoracic idiopathic scoliosis. , 1983, The Journal of bone and joint surgery. American volume.

[3]  Jack C Y Cheng,et al.  The effect of load carriage on the gait of girls with adolescent idiopathic scoliosis and normal controls. , 2006, Medical engineering & physics.

[4]  A. Stacoff,et al.  Gait analysis in patients with idiopathic scoliosis , 2004, European Spine Journal.

[5]  E. Stüssi,et al.  Gait asymmetries in patients with idiopathic scoliosis using vertical forces measurement only , 1998, European Spine Journal.

[6]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[7]  T. Grivas,et al.  Pathogenesis of idiopathic scoliosis. The Nottingham concept. , 1992, Acta orthopaedica Belgica.

[8]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[9]  Paul D Sponseller,et al.  The etiology of adolescent idiopathic scoliosis. , 2002, American journal of orthopedics.

[10]  Josef Havel,et al.  Artificial neural networks based on genetic input selection for quantification in overlapped capillary electrophoresis peaks. , 2004, Talanta.

[11]  S. Nadeau,et al.  Inter-trial and test–retest reliability of kinematic and kinetic gait parameters among subjects with adolescent idiopathic scoliosis , 2008, European Spine Journal.

[12]  David A. Winter,et al.  Biomechanics and Motor Control of Human Movement , 1990 .

[13]  Claire Johnson,et al.  Is physical activity contraindicated for individuals with scoliosis? A systematic literature review. , 2009, Journal of chiropractic medicine.

[14]  Nachiappan Chockalingam,et al.  Assessment of the centre of pressure pattern and moments about S2 in scoliotic subjects during normal walking , 2008, Scoliosis.

[15]  Tomasz Kotwicki,et al.  Trunk rotation and hip joint range of rotation in adolescent girls with idiopathic scoliosis: does the "dinner plate" turn asymmetrically ? , 2008, Scoliosis.

[16]  E. Fogarty,et al.  Adolescent Idiopathic Scoliosis and Cerebral Asymmetry: An Examination of a Nonspinal Perceptual System , 1995, Spine.

[17]  Robert P. W. Duin,et al.  A multi-classifier for grading knee osteoarthritis using gait analysis , 2010, Pattern Recognit. Lett..

[18]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[19]  Jean-Louis Thonnard,et al.  Influence of structural pelvic disorders during standing and walking in adolescents with idiopathic scoliosis. , 2005, The spine journal : official journal of the North American Spine Society.

[20]  Brian T. Smith,et al.  Application of a neuro-fuzzy network for gait event detection using electromyography in the child with Cerebral palsy , 2005, IEEE Transactions on Biomedical Engineering.

[21]  C. Detrembleur,et al.  Gait in adolescent idiopathic scoliosis: kinematics and electromyographic analysis , 2009, European Spine Journal.

[22]  T. T. Soong,et al.  State-of-the-art review: Active structural control in civil engineering , 1988 .

[23]  Young-Seuk Park,et al.  Review of the Self-Organizing Map (SOM) approach in water resources: Commentary , 2009, Environ. Model. Softw..

[24]  S. Weinstein,et al.  Infantile and juvenile scoliosis. , 1999, The Orthopedic clinics of North America.

[25]  Ahnryul Choi,et al.  Ground reaction forces predicted by using artificial neural network during asymmetric movements , 2013 .

[26]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .

[27]  Kemal Polat,et al.  A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis , 2007, Comput. Biol. Medicine.

[28]  G RITCHIE,et al.  Hemangiomatosis of the skeleton and the spleen. , 1956, The Journal of bone and joint surgery. American volume.

[29]  Marimuthu Palaniswami,et al.  Automatic Recognition of Gait Patterns Exhibiting Patellofemoral Pain Syndrome Using a Support Vector Machine Approach , 2009, IEEE Transactions on Information Technology in Biomedicine.

[30]  Joung Hwan Mun,et al.  Asymmetric loading of erector spinae muscles during sagittally symmetric lifting , 2009 .

[31]  Nachiappan Chockalingam,et al.  Assessment of ground reaction force during scoliotic gait , 2004, European Spine Journal.

[32]  Jaw-Lin Wang,et al.  The postural stability control and gait pattern of idiopathic scoliosis adolescents. , 1998, Clinical biomechanics.

[33]  Holger R. Maier,et al.  Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach , 2009 .

[34]  Ashish Sharma,et al.  Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification , 2000 .

[35]  Holger R. Maier,et al.  Non-linear variable selection for artificial neural networks using partial mutual information , 2008, Environ. Model. Softw..

[36]  James Cho Hong Goh,et al.  The repeatablity of spinal motion of normal and scoliotic adolescents during walking. , 2006, Gait & posture.

[37]  R. G. Burwell,et al.  THE PATHOGENESIS OF IDIOPATHIC SCOLIOSIS , 1982 .

[38]  Donald A. Waterman,et al.  A Guide to Expert Systems , 1986 .

[39]  B. Nigg,et al.  Asymmetries in ground reaction force patterns in normal human gait. , 1989, Medicine and science in sports and exercise.

[40]  M. KaltehA.,et al.  Review of the self-organizing map (SOM) approach in water resources , 2008 .

[41]  M S Moreland,et al.  Spinal deformity and back surface asymmetry in idiopathic scoliosis , 1988, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[42]  I A Stokes,et al.  Axial rotation component of thoracic scoliosis , 1989, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[43]  Joung Hwan Mun,et al.  A multi-camera calibration method using a 3-axis frame and wand , 2012 .

[44]  Robert A. Dickson,et al.  Pathogenesis of Idiopathic Scoliosis , 2010 .

[45]  Joung Hwan Mun,et al.  A position based kinematic method for the analysis of human gait , 2005 .