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.

A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information

“…Lim et al [33] developed an insole sensor system that can determine various dynamic models of a lower extremity exoskeleton. Altilio et al [34] proposed a feature selection method to evaluate all the possible combinations of the gait parameters, in order to find the best subset able to classify among diseased and healthy subjects. In this method, medical data is retrieved from a stereophotogrammetric system.…”

Smart Computational Approaches with Advanced Feature Selection Algorithms for Optimizing the Classification of Mobility Data in Health Informatics

“…Feature selection and dimensionality reduction methods have been studied in the gait domain for a variety of applications, including activity recognition [13] and classification of different populations' gait [2,35]. These techniques have been used for identifying the best subset of features from the gait data collected using infrared markers [1,2], video images [32], ground reaction force mats [35], and wearable sensors [5,23]. Among the studies that used the data collected from wearable sensors to distinguish between the gait of healthy individuals and pathological gait, Caramia et al [5] investigates the various subsets of features on several classifiers' performance and reports that the subset of features built using PCA outperforms the other subsets of features made using the domain knowledge.…”

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

“…Altilio et al and Aoike et al used the decision tree algorithm to classify the gait types of the elderly using pressure sensors, acceleration, and gyro data. (15,16) However, because the method was classified by the experimenter according to the set conditions, there was a problem of incorrect classification of some types of gait. Xin et al used discriminant analysis algorithms and a pressure sensor to classify gait; however, because only one pressure sensor was used, it could not accurately reflect the characteristics of the gait phase.…”