Application of principal component analysis in vertical ground reaction force to discriminate normal and abnormal gait.
Discrete parameters from ground reaction force (GRF) are been considered in gait analysis studies. However, principal component analysis (PCA) may provide additional insight into gait analysis for considering the complete pattern of GRF. This study aimed at testing the application of PCA to discriminate the vertical GRF pattern between control group (CG) and patients with lower limb fractures (FG), as well as proposing a score to quantify the abnormality of gait. Thirty-eight healthy subjects participated of CG and 13 subjects in FG, five subjects from FG were also evaluated after physiotherapeutic treatment (FGA). The GRF was measured by an instrumented treadmill. Principal component coefficients (PCCs) were obtained by singular value decomposition using GRF of complete stride. Two, four and six PCCs were used to obtain the standard distance (D). The classification between groups was mainly given by the first PC, which indicated higher loading factors during push off of affected side and heel strike of unaffected side. The classification performance achieved 92.2% accuracy with two PCCs, 94.1% with four PCCs and 96.1% with six PCCs. Four subjects reached normal boundary after treatment, with all FGA subjects presenting decreased D. This study demonstrates that PCA is an adequate method for discriminating normal and abnormal gait and D allows an objective evaluation of the progress and effectiveness of rehabilitation treatment.
Muniz
A M S
AM
Federal University of Rio de Janeiro, Biomedical Engineering Program, COPPE, Rio de Janeiro, Brazil.
Nadal
J
J
D000293:Adolescent; D000328:Adult; D005260:Female; D050723:Fractures, Bone / Q000503:physiopathology* / Q000534:rehabilitation; D005684:Gait / Q000502:physiology*; D006801:Humans; D007869:Leg Injuries / Q000503:physiopathology* / Q000534:rehabilitation; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D018709:Statistics, Nonparametric