1. Clemson University
Department of Bioengineering
Clemson, SC 29634
Quantification of Leg Muscle Forces and Joint Reactions Using a Novel Musculoskeletal Model of Human Cycling Motion
Taylor Gambon1, Jessica Myers1, Randy Hutchison, Ph.D2, John D. DesJardins, Ph.D1
1Clemson University, Clemson, SC, USA, 2Furman University, Greenville, SC, USA
Introduction
Figure 1 Qualisys Model
Figure 2 Visual 3D Model
Results
Musculoskeletal modeling allows for the estimation of many muscle forces as well as joint angles and reactions through a process called inverse dynamic analysis. This process does calculations about forces based on 3D motion and programmed definitions of the body’s environment.
The goal of this project was to create a musculoskeletal model of human cycling motion able to estimate muscle forces and joint reactions based on 3D kinematic trajectory data from an ACL rehabilitation study. An inverse dynamic analysis of the model was performed utilizing AnyBody Technology’s (Aalborg, Denmark) musculoskeletal modeling software (version ).
With definitions of the environment complete, both the Motion and Parameter Optimization sequence and Inverse Dynamic Analysis sequence were validated by the exhibition of traditional knee flexion moment waveforms expected from cycling from the preliminary motion data.
Knee
Flexion
Extension
Preliminary Knee Flexion Moment Data
Time Step
Moment (Nˑm)
Moment (Nm)
Figure 4 Final AnyBody Model
Figure 3 Prelim. AnyBody Model
Knee Flexion Moments From Literature [1]
Degree of Crank Rotation
Materials and Methods
Discussion
Future work will include further validation of the model by comparing electromyography data of the rectus femoris, vastus medialis, vastus lateralis, semitendinosus, biceps femoris, and gastroc from subject testing to the associated estimated forces from the model with regard to ACL rehabilitation loading.
Beginning with the MoCapModel template from the AnyBody Managed Model Repository (version 1.6.3), a full body model was fit to the 3D marker data from the ACL rehabilitation study, using the length-mass-fat scaling setting.
Motion and Parameter Optimization sequencing was completed after approximating the model’s initial joint angles to fit the 3D marker trajectories.
Definitions of the body’s interactions with the bicycle were determined using three areas of interaction.
1) the subjects’ pelvis in contact with the bicycle seat, 2) the subject’s hands on the handlebars, and 3) the subject’s feet on the pedals. The interactions at the pelvis and the hands were addressed with a conditional contact class which accounted for the normal forces at these two locations. The pedals were addressed by force pedal data collected during subject testing.
Unlike gait studies, the force plates for this experimental setup moved with the cycle pedals as opposed to being stationary during the capture period. In order to define this motion for the force plates, the ForcePlateType4 class was modified. Instead of driving the force plate to a constant position defined by the calibration file, kinematic drivers were added to connect the pedal segments to the 3D marker trajectories of the pedals from the motion data.
References
[1] Bini, R. R., & Diefenthaeler, F (2010). Kinetics and kinematics analysis of incremental cycling to exhaustion Sports Biomechanics / International Society Of Biomechanics In Sports, 9(4), doi: /
Presented at the Sigma Xi Student Research Conference on Nov 8, 2014
Glendale, Arizona
Corresponding Author: Dr. John DesJardins, Clemson University, Bioengineering Department
301 Rhodes Building, Clemson, SC 29634, Special thanks to Furman University for collaboration and use of the Molnar Human Performance Laboratory