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


METHOD
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).


RESULTS
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.


CONCLUSION
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.
['Aljohani|Marwan|M|', 'Kipp|Kristof|K|']
D000293:Adolescent D000328:Adult D000843:Ankle Joint D001696:Biomechanical Phenomena D004576:Electromyography D005080:Exercise Test D005260:Female D006801:Humans D007717:Knee D007719:Knee Joint D007866:Leg D035002:Lower Extremity D000069550:Machine Learning D008297:Male D018482:Muscle, Skeletal D016571:Neural Networks, Computer D012307:Risk Factors D012420:Running D012737:Sex Factors D013977:Tibia D055815:Young Adult
Injury Artificial neural network Coordination Machine learning Sex differences