Development of a Planar Piecewise Continuous Lumped Muscle Parameter Model for Investigation of Joint Stiffness in Walking on a Level Surface
Fu, Qianyi
2020
Abstract
When joint stiffnesses are affected by injuries or illnesses they can interfere with gait and with activities of daily living, work, and leisure. Biomechanical models have been proposed for describing the effects of various conditions and interventions on the phases of gait. This dissertation reports the development of a planar piecewise continuous lumped muscle parameter (PPCLMP) model for investigating how different joint stiffnesses affect the gait phases individually and collectively. The proposed PPCLMP model characterizes the movements of lower limbs during each gait phase by a simplified dynamic system: the single stance phase by an inverted pendulum, the double stance phase by a kinematic chain, and the swing phase by a double pendulum. The model uses lumped muscle parameters to characterize the joint torques during each phase. The phase continuity is achieved by setting the joint angles and angular velocities at the end of one phase equal to those at the start of the next phase. The model can predict gait movements from given initial conditions (initial joint angles and angular velocities), anthropometry, lumped muscle parameters, and joint stiffness in a forward-dynamic mode. Also, if the gait movements are known, the model could estimate the lumped muscle parameters in an inverse dynamic mode. In the first study, the model was used in the forward-dynamic mode to predict joint angles and gait parameters for six healthy subjects’ anthropometry, ankle joint stiffnesses (without ankle-foot orthosis (AFO), with a low-stiffness AFO, and with a high-stiffness AFO), initial conditions, and constant lumped muscle parameters. Results showed that the trend of gait parameters changings (longer step length and shorter swing time on the AFO side for higher AFO stiffness) with different AFO stiffnesses were qualitatively well predicted by the model but quantitative prediction accuracy was limited (the mean errors were 0.15 m and 5% for the predicted step length and swing time, respectively) due to the constant values of lump muscle parameters. The second study examined the use of the model in an inverse-dynamic mode using data from a single inertial measurement unit (IMU) attached to the lower shank in order to estimate the initial conditions and lumped muscle parameters for each gait cycle. These were used by the model in the forward-dynamic mode to enhance the gait prediction. Results from two patients wearing AFOs demonstrated that the model prediction was markedly improved comparing with the first study by utilizing the inverse-dynamic mode as the mean RMSE was 0.07 m and 2% for predicted step length and swing time, respectively. The third study investigated the PPCLMP model prediction accuracy using the inverse and forward dynamic processes proposed in the second study. Three male and three female healthy subjects were recruited to walk with IMU-instrumented AFOs on their left feet to measure step lengths and swing time, while surface electrodes measured selected muscle activities for comparison with lumped muscle parameters. Results showed that the model prediction accuracy of step lengths and walking speed improved significantly (p < 0.05) with increasing stature; however, model prediction accuracy of swing time unaffected by stature. It was concluded that the PPCLMP model of gait has the potential for predicting how the prescription of an AFO of a given stiffness will affect gait, but more research is needed to refine model predictions by improving the representation of joint torques during gait.Subjects
Biomechanical Modeling Gait Analysis and Predictioin Ankle-Foot Orthosis Lumped Parameters Forward and Inversed Dynamics Inertial Measurement Units
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