title: Selection of clinical features for pattern recognition applied to gait analysis.
abstract: This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis. A feature selection method to exhaustively evaluate all the possible combinations of the gait parameters is presented, in order to find the best subset able to classify among diseased and healthy subjects. This procedure will be used for estimating the performance of widely used classification algorithms, whose performance has been ascertained in many real-world problems with respect to well-known classification benchmarks, both in terms of number of selected features and classification accuracy. Precisely, support vector machine, Naive Bayes and K nearest neighbor classifiers can obtain the lowest classification error, with an accuracy greater than 97 %. For the considered classification problem, the whole set of features will be proved to be redundant and it can be significantly pruned. Namely, groups of 3 or 5 features only are able to preserve high accuracy when the aim is to check the anomaly of a gait. The step length and the swing speed are the most informative features for the gait analysis, but also cadence and stride may add useful information for the movement evaluation.
authors.affiliation: Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome "La Sapienza", Via Eudossiana, 18, 00184, Rome, Italy. rosa.altilio@uniroma1.it.
authors.forename: Rosa
authors.initials: R
authors.lastname: Altilio
authors.affiliation: Biomechanics and Movement Analysis Laboratory, Physical Medicine and Rehabilitation, University of Rome "La Sapienza", Piazzale Aldo Moro, 5, 00185, Rome, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Paoloni
authors.affiliation: Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome "La Sapienza", Via Eudossiana, 18, 00184, Rome, Italy.
authors.forename: Massimo
authors.initials: M
authors.lastname: Panella
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D016022:Case-Control Studies; D005260:Female; D005684:Gait / Q000502:physiology*; D006429:Hemiplegia / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods*; D009103:Multiple Sclerosis / Q000503:physiopathology; D015207:Osteoarthritis, Hip / Q000503:physiopathology; D010300:Parkinson Disease / Q000503:physiopathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D060388:Support Vector Machine; D055815:Young Adult
keywords: Classification; Feature selection; Gait analysis; Pattern recognition