Recognition of Foot-Ankle Movement Patterns in Long-Distance Runners With Different Experience Levels Using Support Vector Machines

Abstract

Running practice could generate musculoskeletal adaptations that modify the body mechanics and generate different biomechanical patterns for individuals with distinct levels of experience. Therefore, the aim of this study was to investigate whether foot-ankle kinetic and kinematic patterns can be used to discriminate different levels of experience in running practice of recreational runners using a machine learning approach. Seventy-eight long-distance runners (40.7±7.0yrs) were classified into less experienced (n=24), moderately experienced (n=23) or experienced (n=31) runners using a fuzzy classification system, based on training frequency, volume, competitions and practice time. Three-dimensional kinematics of the foot-ankle and GRF were acquired while the subjects ran on an instrumented treadmill at a self-selected speed (9.5-10.5km/h). The foot-ankle kinematic and kinetic time series underwent a principal component analysis for data reduction, and combined with the discrete GRF variables to serve as inputs in a support vector machine (SVM), to determine if the groups could be distinguished between them in a one-vs-all approach. In addition, 33 discrete biomechanical variables were extracted and compared between the experience groups using ANOVAs followed by Bonferroni post-hoc tests (P<0.05). Univariate analysis approach showed no between-group differences for the discrete variables. The SVM models successfully classified all experience groups with significant cross-validated accuracy rates and strong to very strong Matthew’s correlation coefficients, based on features from the input data. Overall, foot mechanics was different according to running experience level. The main distinguishing kinematic factors for the less experienced group were a greater dorsiflexion of the first metatarsophalangeal joint and a larger plantarflexion angles between the calcaneus and metatarsals, whereas the experienced runners displayed the opposite pattern for the same joints. The fact that only the multivariate analysis approach identified differences in foot-ankle movement patterns between groups suggests that the combination of variables and the relationship between them can be more effective in identifying running patterns. The current approach could be applied in other studies involving movement analysis to better identify mechanical changes due to therapeutic interventions. The results of this study have the potential to assist the development of training programs targeting improvement in performance and rehabilitation protocols for preventing injuries.