Use of self-organizing maps to study sex- and speed-dependent changes in running biomechanics.

Abstract

Up to 79% of runners get injured every year, with higher rates of injuries occurring in females than males. A self-organizing map (SOM) is a type of artificial neural network that can be used to inspect large datasets and study coordination patterns. The purpose of this study was to use an SOM to study the effects of sex and speed on biomechanical coordination patterns. Thirty-two healthy runners ran on an instrumented treadmill at their long slow distance speed (LSD) and at speed 30% faster (LSD+30%). Vertical ground reaction force (vGRF), vertical tibial acceleration, step parameters, electromyograms (EMG) of six lower limb muscles, and joint angles were collected across speeds. Rate of loading (ROL), tibial impact shock (TIS), coupling angle variability (CAV) and movement pattern proportions for hip/knee sagittal and hip frontal / knee sagittal plane couplings, peak EMG, step length, step rate, and knee and ankle joint angle at initial contact were used as an input for the SOM (37 variables). The analysis identified four clusters (i.e., running patterns). While males and females showed similar distribution across clusters at LSD (p=.36) and at LSD+30% (p=.51), females did exhibit a significant (p=.03) shift between clusters as the speed increased from LSD to LSD+30% whereas males did not (p=.17). The shift was associated with an increase in TIS, ROL, step length, step rate, vastus lateralis EMG, hip flexion/knee extension movement pattern proportion, and a decrease in ST EMG and CAVIC for hip sagittal/knee sagittal coupling. As running speed increased there was a significant change in the coordination pattern in females, which was characterized by increases in several variables that are purported risk factors for running related injuries.