Dataset Information

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 ± 7.0 years) 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 ground reaction forces (GRF) were acquired while the subjects ran on an instrumented treadmill at a self-selected speed (9.5-10.5 km/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. The SVM models successfully classified all experience groups with significant crossvalidated 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. As for the moderately experienced runners, although they were successfully classified, they did not present a visually identifiable running pattern, and seem to be an intermediate group between the less and more experienced runners. 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.

SUBMITTER: Suda EY

PROVIDER: S-EPMC7300177 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Suda Eneida Yuri EY Watari Ricky R Matias Alessandra Bento AB Sacco Isabel C N ICN

Frontiers in bioengineering and biotechnology 20200611

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 ± 7.0 years) were classified ...[more]

PMID: 32596226

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Project description:BackgroundFor future etiologic cohort studies in runners it is important to identify whether (hyper)pronation of the foot, decreased ankle joint dorsiflexion (AJD) and the degree of the extension of the first Metatarsophalangeal joint (MTP1) are risk factors for running injuries and to determine possible sex differences.These parameters are frequently determined with the navicular drop test (NDT) Stance and Single Limb-Stance, the Ankle Joint Dorsiflexion-test, and the extension MTP1-test in a healthy population. The aim of this clinimetric study was to determine the reproducibility of these three orthopaedic tests in runners, using minimal equipment in order to make them applicable in large cohort studies. Furthermore, we aimed to determine possible sex differences of these tests.MethodsThe three orthopaedic tests were administered by two sports physiotherapists in a group of 42 (22 male and 20 female) recreational runners. The intra-class correlation (ICC) for interrater and intrarater reliability and the standard error of measurement (SEM) were calculated. Bland and Altman plots were used to determine the 95% limits of agreements (LOAs). Furthermore, the difference between female and male runners was determined.ResultsThe ICC's of the NDT were in the range of 0.37 to 0.45, with a SEM in the range of 2.5 to 5 mm. The AJD-test had an ICC of 0.88 and 0.86 (SEM 2.4° and 8.7°), with a 95% LOA of -6.0° to 6.3° and -5.3° to 7.9°, and the MTP1-test had an ICC of 0.42 and 0.62 (SEM 34.4° and 9.9°), with a 95% LOA of -30.9° to 20.7° and -20° to 17.8° for the interrater and intrarater reproducibility, respectively.Females had a significantly (p<0.05) lower navicular drop score and higher range of motion in extension of the MTP1, but no sex differences were found for ankle dorsiflexion (p ? 0.05).ConclusionThe reproducibility for the AJD test in runners is good, whereas that of the NDT and extension MTP1 was moderate or low. We found a difference in NDT and MTP1 mobility between female and male runners, however this needs to be established in a larger study with more reliable test procedures.

Project description:BACKGROUND:While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross-validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un-annotated genes. A total of approximately 5043 different genes, or about one-third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un-annotated. RESULTS:39 Gene Ontology Biological Process (GO-BP) categories were found with precision value equal or larger than 0.75, when recall was fixed at the 0.4 level. For two of those categories, we have provided additional support for assigning given genes to the category by showing that the majority of transcripts for the genes belonging in a given category have a similar localization pattern during embryogenesis. Additionally, by assessing the predictions using a confidence score, we have been able to provide a putative GO-BP term for 1422 previously un-annotated genes or about 77% of the un-annotated genes represented on the microarray and about 19% of all of the un-annotated genes in the D. melanogaster genome. CONCLUSIONS:Our study successfully employs a number of SVM classifiers, accompanied by detailed calibration and validation techniques, to generate a number of predictions for new annotations for D. melanogaster genes. The applied probabilistic analysis to SVM output improves the interpretability of the prediction results and the objectivity of the validation procedure.

Dataset Information

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

Publications

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

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