Analysis of muscle coordination strategies in cycling.
The functional significance of the stereotypical muscle activation patterns used in skilled multi-joint tasks is not well understood. Optimization methods could provide insight into the functional significance of muscle coordination. The purpose of this study was to predict muscle force patterns during cycling by pushing and pulling the pedal using different optimization criteria and compare the predictions with electromyographic (EMG) patterns. To address the purpose of the study, 1) the contribution of muscle length and velocity changes to EMG-muscle force relationships during cycling was examined by comparing joint moments calculated from EMG and inverse dynamics, 2) patterns of individual muscle forces during cycling of five subjects were predicted using 13 different optimization criteria, and 3) the properties of the criterion with the best performance in predicting the normalized EMG were used to explain the features and functional significance of muscle coordination in cycling. It was shown that the criterion that minimizes the sum of muscle stresses cubed demonstrated the best performance in predicting the relative magnitude and patterns of muscle activation. Based on this criterion, it was suggested that the functional significance of muscle coordination strategy in cycling may be minimization of fatigue and/or perceived effort.
Prilutsky
B I
BI
Department of Health and Performance Sciences, Center for Human Movement Studies, Georgia Institute of Technology, Atlanta 30332, USA. boris.prilutsky@hps.gatech.edu
Gregory
R J
RJ
D000328:Adult; D015982:Bias; D001642:Bicycling / Q000502:physiology*; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D004576:Electromyography; D005260:Female; D006801:Humans; D007866:Leg / Q000502:physiology*; D008297:Male; D008954:Models, Biological; D009119:Muscle Contraction / Q000502:physiology*; D018763:Muscle Fatigue / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology*; D005082:Physical Exertion / Q000502:physiology; D011237:Predictive Value of Tests; D011597:Psychomotor Performance / Q000502:physiology*; D012399:Rotation; D012815:Signal Processing, Computer-Assisted