Classification of gait kinematics of anterior cruciate ligament reconstructed subjects using principal component analysis and regressions modelling

The aim of this study was to compare the knee kinematics of anterior cruciate ligament reconstructed (ACL-R) and healthy subjects (CG) during gait and classify the status of normality. Ten healthy and six ACL-R subjects had their gait analyzed at 60 fps. 3D knee angles were calculated and inserted into three separate matrices used to perform the principal component (PC) analysis. The scores of PCs retained in each analysis were used to calculate the standard distances (SD) of each participant in relation to the center of the CG. The PC scores of the three planes were used in a logistic regression to define normality. In the sagittal plane there was no difference between groups. In the frontal and transverse planes ACL-R subjects showed higher SD values than CG. PCs identified that ACL-R subjects showed increased adduction, internal and external rotation. All these subjects had their gait classified as abnormal by logistic regression. Therefore, in the studied ACL-R subjects the gait pattern did not return to normal levels after surgery. This may lead to degenerative injuries, as osteoarthritis, in the future.

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

[2]  Thomas P Andriacchi,et al.  Rotational Changes at the Knee after ACL Injury Cause Cartilage Thinning , 2006, Clinical orthopaedics and related research.

[3]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[4]  W. Vach,et al.  Neural networks and logistic regression: Part I , 1996 .

[5]  T Chau,et al.  A review of analytical techniques for gait data. Part 2: neural network and wavelet methods. , 2001, Gait & posture.

[6]  Mark R Cutkosky,et al.  Training multi-parameter gaits to reduce the knee adduction moment with data-driven models and haptic feedback. , 2011, Journal of biomechanics.

[7]  M. Axe,et al.  The effect of insufficient quadriceps strength on gait after anterior cruciate ligament reconstruction. , 2002, Clinical biomechanics.

[8]  H. Riedwyl,et al.  Standard Distance in Univariate and Multivariate Analysis , 1986 .

[9]  J. L. Astephen,et al.  Changes in frontal plane dynamics and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis application of a multidimensional analysis technique. , 2005, Clinical biomechanics.

[10]  R. Baker Gait analysis methods in rehabilitation , 2006, Journal of NeuroEngineering and Rehabilitation.

[11]  J. Nadal,et al.  Gait initiation evaluation after deep brain stimulation for Parkinson's disease: A 7-year follow-up , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[12]  Nicholas Stergiou,et al.  Tibial Rotation in Anterior Cruciate Ligament (ACL)-Deficient and ACL-Reconstructed Knees , 2018 .

[13]  Ciara M O'Connor,et al.  Automatic detection of gait events using kinematic data. , 2007, Gait & posture.

[14]  Luis Mateus Rocha,et al.  Singular value decomposition and principal component analysis , 2003 .

[15]  M. Englund,et al.  High prevalence of knee osteoarthritis, pain, and functional limitations in female soccer players twelve years after anterior cruciate ligament injury. , 2004, Arthritis and rheumatism.

[16]  Jurandir Nadal,et al.  APPLICATION OF PRINCIPAL COMPONENT ANALYSIS IN THE STUDY OF TASKS WITH DIFFERENT MECHANICAL CONSTRAINTS , 2011 .