Flat foot functional evaluation using pattern recognition of ground reaction data

Abstract

Objective. Main purpose of this study was to apply quantitative gait analysis and statistical pattern recognition as clinical decision-making aids in flat foot diagnosis and post-surgery monitoring. Design. Statistical pattern recognition techniques were applied to discriminate between normal and flat foot populations through ground reaction force measurements; ground reaction forces time course was assumed as a sensible index of the foot function. Background. Gait analysis is becoming recognized as an important clinical tool in orthopaedics, in pre-surgery planning, post-surgery monitoring and in a posteriori evaluation of different treatment techniques. Statistical pattern recognition techniques have been utilized with success in this field to identify the most significant variables of selected motor functions in different pathologies, and to design classification rules and quantitative evaluation scores. Methods. Ground reaction forces were recorded during free speed barefoot walks on 28 healthy subjects, and 28 symptomatic flexible flat foot children selected for surgical intervention. A new feature selection algorithm, based on heuristic optimization, was applied to select the most discriminant ground reaction forces time samples. A two-stage pattern recognition system, composed by three linear feature extractors, one for each ground reaction force component, and a linear classifier, was designed to classify the feet of each subject using the selected features. The output of the classifier was used to define a functional score. Results. The classifier assigned the ground reaction force patterns performed by each subject into the right class with an estimated error of 15%, corresponding to an assignment error for each subject’s foot of 9%. The most discriminant ground reaction forces time samples selected are in full agreement with the pathophysiology of the symptomatic flexible flat foot. The obtained score was utilized to monitor the 1 and 2 years post-operative functional recovery of two differently treated subgroups of 32 flexible flat foot subjects. Conclusions. Statistical pattern recognition techniques are promising tools for clinical gait analysis; the obtained score provides important functional information that could be used as a further aid in the clinical evaluation of flat foot and different surgical treatment techniques. Relevance Symptomatic flexible flat foot surgical decision making is frequently difficult because of the lack of objective criteria to assess functional abnormalities of the foot/ankle complex. Gait analysis and statistical pattern recognition can give us parameters with which to characterize “functional” flat foot. Moreover, we can objectively follow up the recovery of the foot/ankle complex function after surgical treatment.