Selection of clinical features for pattern recognition applied to gait analysis

• Published in: Medical & biological engineering & computing Vol. 55; no. 4; pp. 685 - 695
• Main Authors: Altilio, Rosa, Paoloni, Marco, Panella, Massimo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2017; Springer Nature B.V
• Subjects: Accuracy; Adult; Aged; Aged, 80 and over; Algorithms; Analysis; Bayes Theorem; Biomechanics; Biomedical and Life Sciences; Biomedical data; Biomedical engineering; Biomedical Engineering and Bioengineering; Biomedicine; Case-Control Studies; Classification; Computer Applications; Datasets; Estimating; Feature selection; Female; Gait; Gait - physiology; Hemiplegia - physiopathology; Human Physiology; Humans; Image processing systems; Imaging; Laboratories; Male; Middle Aged; Monitoring, Physiologic - methods; Multiple sclerosis; Multiple Sclerosis - physiopathology; Original Article; Osteoarthritis, Hip - physiopathology; Parkinson Disease - physiopathology; Parkinson's disease; Patients; Pattern recognition; Pattern Recognition, Automated - methods; Principal components analysis; Radiology; Rehabilitation; Reproducibility of Results; Stroke; Studies; Support Vector Machine; Support vector machines; Swing; Walking; Young Adult; Feature selection; Pattern recognition; Gait analysis; Classification
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-016-1546-1

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.