Manipulating seat configuration (i.e., seat tube angle, seat height and pelvic orientation) alters the bicycle-rider geometry, which influences lower extremity muscle kinematics and ultimately muscle force and power generation during pedaling. Previous studies have sought to identify the optimal configuration, but isolating the effects of specific variables on rider performance from the confounding effect of rider adaptation makes such studies challenging. Of particular interest is the influence of seat tube angle on rider performance, as seat tube angle varies across riding disciplines (e.g., road racers vs. triathletes). The goals of the current study were to use muscle-actuated forward dynamics simulations of pedaling to 1) identify the overall optimal seat configuration that produces maximum crank power and 2) systematically vary seat tube angle to assess how it influences maximum crank power. The simulations showed that a seat height of 0.76 m (or 102% greater than trochanter height), seat tube angle of 85.1 deg, and pelvic orientation of 20.5 deg placed the major power-producing muscles on more favorable regions of the intrinsic force-length-velocity relationships to generate a maximum average crank power of 981 W. However, seat tube angle had little influence on crank power, with maximal values varying at most by 1% across a wide range of seat tube angles (65 to 110 deg). The similar power values across the wide range of seat tube angles were the result of nearly identical joint kinematics, which occurred using a similar optimal seat height and pelvic orientation while systematically shifting the pedal angle with increasing seat tube angles.
Jeffery W. Rankin and Richard R. Neptune
Steven A. Kautz, Richard R. Neptune and Felix E. Zajac
The target article presents a framework for coordination of one- and two-joint muscles in a variety of tasks. Static optimization analyses were performed that minimize muscle fatigue, and it is claimed that the predicted muscle forces account for essential features of EMG activity “qualitatively” well. However, static optimization analyses use the observed joint moments, which implicitly assumes that they minimize the total muscle fatigue of the task. We use a forward dynamics (i.e., relationship between muscle forces and the kinematics and kinetics of task performance) modeling approach to show that this assumption does not appear to be true in cycling (which was used as an example task in the target article). Our results challenge the hypothesized coordination framework and the underlying concept that general coordination principles for dynamic tasks can be elucidated using inverse-dynamics-based analyses.
Yumeng Li, He Wang and Kathy J. Simpson
. 31 Kinematic constraints were then removed, and a forward dynamics simulation was performed with muscles serving as actuators and experimental GRFs applied to replicate the landing motion. A proportional–integral–derivative feedback controller was implemented to calculate each muscle force magnitude
Paul J. Felton, Maurice R. Yeadon and Mark A. King
and kinetics of a movement with nonsagittal plane rotations of the pelvis and torso. Methods A forward dynamics simulation model of the front foot contact phase of the fast bowling action in cricket was chosen since the movement is predominately planar in all aspects other than the nonsagittal plane