title: Human gait classification after lower limb fracture using Artificial Neural Networks and principal component analysis.
abstract: 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.
authors.affiliation: Biomedical Engineering Program, Federal University of Rio de Janeiro, P. O. Box 68510, ZIP 21941-972, Brazil. krloslozano@peb.ufrj.br
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Lozano-Ortiz
authors.forename: Adriane M S
authors.initials: AM
authors.lastname: Muniz
authors.forename: Jurandir
authors.initials: J
authors.lastname: Nadal
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D050723:Fractures, Bone / Q000175:diagnosis* / Q000503:physiopathology*; D005684:Gait; D007869:Leg Injuries / Q000503:physiopathology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity