Soft tissue motion influences skeletal loads during impacts

Soft tissue motion occurs as impulsive loads are applied to the skeletal system. It has been demonstrated that the wave like motion of these wobbling masses can reduce the loads acting on the musculoskeletal system. This is an important concept to consider, whether the loads acting on the musculoskeletal system are being determined using either inverse or direct dynamics

The influence of soft tissue movement on ground reaction forces, joint torques and joint reaction forces in drop landings

The aim of this study was to determine the effects that soft tissue motion has on ground reaction forces, joint torques and joint reaction forces in drop landings. To this end a four body-segment wobbling mass model was developed to reproduce the vertical ground reaction force curve for the first 100 ms of landing. Particular attention was paid to the passive impact phase, while selecting most model parameters a priori, thus permitting examination of the rigid body assumption on system kinetics. A two-dimensional wobbling mass model was developed in DADS (version 9.00, CADSI) to simulate landing from a drop of 43 cm. Subject specific inertia parameters were calculated for both the rigid links and the wobbling masses. The magnitude and frequency response of the soft tissue of the subject to impulsive loading was measured and used as a criterion for assessing the wobbling mass motion. The model successfully reproduced the vertical ground reaction force for the first 100 ms of the landing with a peak vertical ground reaction force error of 1.2 % and root mean square errors of 5% for the first 15 ms and 12% for the first 40 ms. The resultant joint forces and torques were lower for the wobbling mass model compared with a rigid body model, up to nearly 50% lower, indicating the important contribution of the wobbling masses on reducing system loading

Comparison of positive joint work performed at each joint in the two foot models.

The multi-segment foot model (MULTI) is the left bar group, and the single segment foot model (SINGLE) is the right bar. Ankle positive work (dark blue) was significantly different between the foot models, but the summed ankle and midfoot from MULTI (gray bar) was similar to ankle joint work from SINGLE (dark blue), suggesting that SINGLE captured midfoot joint power in the ankle joint power.</p

Definitions of unit vectors used to define segment reference frames for the segments of the multi-segment foot model.

The single segment foot model shares the same reference frame definition as the rearfoot to standardize the ankle joint convention. See Fig 1 for marker locations.</p

Fig 2 -

Comparison of ankle angles during stance computed using two different foot models: SINGLE—single segment foot (solid blue), MULTI—multi-segment foot (dotted red). Data computed from barefoot running at 3.1 m/s. Shaded regions show ± 1 S.D. The top row shows ankle angles throughout the stance phase. Angle conventions are inversion(+)/eversion(-), abduction(+)/adduction(-), and dorsiflexion(+)/plantarflexion(-). The bottom row shows results from paired t-tests btween SINGLE and MULTI, with the red dotted lines representing the t-statistic threshold for statistical significance. Gray shaded areas outside of the red dotted lines show regions with significant differences between SINGLE and MULTI.</p

The influence of model parameters on model validation

The study examined the sensitivity of two musculoskeletal models to the parameters describing each model. Two different models were examined: a phenomenological model of human jumping with parameters based on live subject data, and the second a model of the First Dorsal Interosseous with parameters based on cadaveric measurements. Both models were sensitive to the model parameters, with the use of mean group data not producing model outputs reflective of either the performance of any group member or the mean group performance. These results highlight the value of subject specific model parameters, and the problems associated with model validation