title: Computer aided analysis of gait patterns in patients with acute anterior cruciate ligament injury.
abstract: BACKGROUND
  Gait analysis is a useful tool to evaluate the functional status of patients with anterior cruciate ligament injury. Pattern recognition methods can be used to automatically assess walking patterns and objectively support clinical decisions. This study aimed to test a pattern recognition system for analyzing kinematic gait patterns of recently anterior cruciate ligament injured patients and for evaluating the effects of a therapeutic treatment.
  
  
  METHODS
  Gait kinematics of seven male patients with an acute unilateral anterior cruciate ligament rupture and seven healthy males were recorded. A support vector machine was trained to distinguish the groups. Principal component analysis and recursive feature elimination were used to extract features from 3D marker trajectories. A Classifier Oriented Gait Score was defined as a measure of gait quality. Visualizations were used to allow functional interpretations of characteristic group differences. The injured group was evaluated by the system after a therapeutic treatment. The results were compared against a clinical rating of the patients' gait.
  
  
  RESULTS
  Cross validation yielded 100% accuracy. After the treatment the score improved significantly (P<0.01) as well as the clinical rating (P<0.05). The visualizations revealed characteristic kinematic features, which differentiated between the groups.
  
  
  CONCLUSIONS
  The results show that gait alterations in the early phase after anterior cruciate ligament injury can be detected automatically. The results of the automatic analysis are comparable with the clinical rating and support the validity of the system. The visualizations allow interpretations on discriminatory features and can facilitate the integration of the results into the diagnostic process.
authors.affiliation: Department of Sports Science and Kinesiology, University of Salzburg, Schlossallee 49, 5400 Hallein-Rif, Austria. Electronic address: josef.christian@sbg.ac.at.
authors.forename: Josef
authors.initials: J
authors.lastname: Christian
authors.affiliation: Department of Sports Science and Kinesiology, University of Salzburg, Schlossallee 49, 5400 Hallein-Rif, Austria. Electronic address: josef.kroell@sbg.ac.at.
authors.forename: Josef
authors.initials: J
authors.lastname: Kröll
authors.affiliation: Department of Sports Science and Kinesiology, University of Salzburg, Schlossallee 49, 5400 Hallein-Rif, Austria. Electronic address: gerda.strutzenberger@sbg.ac.at.
authors.forename: Gerda
authors.initials: G
authors.lastname: Strutzenberger
authors.affiliation: Department of Sports Science and Kinesiology, University of Salzburg, Schlossallee 49, 5400 Hallein-Rif, Austria. Electronic address: nathalie.alexander@sbg.ac.at.
authors.forename: Nathalie
authors.initials: N
authors.lastname: Alexander
authors.affiliation: Institute of Pathophysiology and Immunology, Medical University of Graz, Heinrichstraße 31a, 8010 Graz, Austria. Electronic address: michael.ofner@medyco.net.
authors.forename: Michael
authors.initials: M
authors.lastname: Ofner
authors.affiliation: Department of Sports Science and Kinesiology, University of Salzburg, Schlossallee 49, 5400 Hallein-Rif, Austria. Electronic address: hermann.schwameder@sbg.ac.at.
authors.forename: Hermann
authors.initials: H
authors.lastname: Schwameder
mesh_terms: D000328:Adult; D016118:Anterior Cruciate Ligament / Q000503:physiopathology*; D000070598:Anterior Cruciate Ligament Injuries; D001696:Biomechanical Phenomena; D000066491:Clinical Decision-Making; D003658:Decision Making, Computer-Assisted; D005684:Gait / Q000502:physiology*; D006801:Humans; D007719:Knee Joint / Q000503:physiopathology*; D008297:Male; D025341:Principal Component Analysis; D016138:Walking / Q000502:physiology
keywords: Anterior cruciate ligament; Gait; Kinematics; Pattern recognition