Support vector machines for detecting age-related changes in running kinematics

Abstract

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts.

Do intermediate- and higher-order principal components contain useful information to detect subtle changes in lower extremity biomechanics during running?

“…3). These results are similar to previous studies that indicated age-related changes in discrete joint angles are usually observed in the sagittal plane (Fukuchi, Eskofier, Duarte, & Ferber, 2011;Nigg et al, 2012). Interestingly, PC 1 was also 73% loaded on hip joint and pelvis segment angles (Fig.…”

Older Runners Retain Youthful Running Economy despite Biomechanical Differences

“…Data on young runners suggest an association between reduced flexibility and enhanced running economy (13, 25, 52). Yet, only a few investigations have measured the biomechanics of runners beyond 65 years of age (21, 22, 34), and none of them report leg stiffness (k leg ). Other biomechanical variables have also been correlated with the metabolic cost of running including average vertical ground reaction force (GRF) (33), braking and propulsive GRF (11), contact time (t c ) (49), and stride frequency (10).…”

Defining functional groups based on running kinematics using Self-Organizing Maps and Support Vector Machines

“…Age and gender were frequently used for pregrouping individuals (Begg and Kamruzzaman, 2005;Eskofier et al, 2011b;Fukuchi et al, 2011). Afterwards, the movement patterns were compared across these predefined groups to determine if there were differences.…”