The overall goal of this project was to develop a computer simulation of normal human walking that would use as driving moments the resultant joint moments calculated in a 3-D gait analysis. Accordingly, three component projects were undertaken. The first study tested the feasibility of using data generated by an inverse dynamics analysis to drive a computer simulation. Artificial displacement data were created using a direct dynamics analysis of three different three-segment models: constant moments were applied and predicted kinematics were generated. Displacement data for specific points on the model were then run through an inverse dynamics analysis, both with and without random noise added. The calculated joint moments were used to drive a second simulation. It was found that even a small amount of error in the displacement data resulted in an extremely poor prediction of the kinematics by the second simulation. The conclusion drawn was that system controllers are necessary to run successful simulations of this type.
The second study was the development of a foot model suitable for use in a gait simulation. For this, ankle joint forces and moments were used to run simulations of one foot in isolation. The foot was divided into two parts with nine visco-elastic elements aligned along the midline. Torsional dampers controlled the rotation about each axis. The displacement patterns and the ground reaction forces predicted by the simulation closely matched the measured data for a complete stance phase.
The third study encompassed the development of a nine-segment, 3-D gait simulation. The hip, knee and ankle joints were modelled as three, one and two dcgrce-of-freedom joints, respectively. The foot model described above was incorporated with only minor modifications. The 'controllers' used were all torsional, linear springs and/or dampers at various joints. Irunk controllers altered the hip joint moments to keep the trunk vertical. Range-of-motion controllers prevented non-physiological motion at the knees and ankles. Other dampers were required to control the motion, particularly during the double support phase. The simulated human successfully completed one step (550 ms).
|1990||Winter DA, Ruder GK, MacKinnon CD. Control of balance of upper body during gait. In: Winters JM, Woo SL-Y, eds. Multiple Muscle Systems: Biomechanics and Movement Organization. New York, NY: Springer-Verlag; 1990:534-541.|
|1990||Bell AL, Pedersen DR, Brand RA. A comparison of the accuracy of several hip center location prediction methods. J Biomech. 1990;23(6):617-621.|
|1974||Winter DA, Sidwall HG, Hobson DA. Measurement and reduction of noise in kinematics of locomotion. J Biomech. March 1974;7(2):157-159.|
|1987||Davy DT, Audu ML. A dynamic optimization technique for predicting muscle forces in the swing phase of gait. J Biomech. 1987;20(2):187-201.|
|1991||Meglan DA. Enhanced Analysis of Human Locomotion [PhD thesis]. The Ohio State University; 1991.|
|1989||van den Bogert AJ. Computer Simulation of Locomotion in the Horse [PhD thesis]. Utrecht University; 1989.|
|1990||Kadaba MP, Ramakrishnan HK, Wootten ME. Measurement of lower extremity kinematics during level walking. J Orthop Res. May 1990;8(3):383-392.|
|1988||Veldpaus FE, Woltring HJ, Dortmans JMG. A least-squares algorithm for the equiform transformation from spatial marker co-ordinates. J Biomech. 1988;21(1):45-54.|
|1990||Yamaguchi GT. Performing whole-body simulations of gait with 3-D, dynamic musculoskeletal models. In: Winters JM, Woo SL-Y, eds. Multiple Muscle Systems: Biomechanics and Movement Organization. New York, NY: Springer-Verlag; 1990:663-679.|
|2002||Camacho DLA, Ledoux WR, Rohr ES, Sangeorzan BJ, Ching RP. A three-dimensional, anatomically detailed foot model: a foundation for a finite element simulation and means of quantifying foot-bone position. J Rehab Res Dev. 2002;39(3):401-410.|
|1999||Ledoux WR II. A Biomechanical Model of the Human Foot With Emphasis on the Plantar Soft Tissue [PhD thesis]. Philadelphia, PA: University of Pennsylvania; 1999.|