Team Members
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Background
In the field of biomechanics, understanding the intricate mechanisms involved in human locomotion has long been a subject of interest. One fundamental aspect of this research is investigating how muscle activations vary between different walking conditions. In particular, this project looks to explore the disparities in muscle activation patterns during unloaded and loaded walking. This investigation aims to shed light on the adaptations that occur in the neuromuscular system when individuals carry external loads while walking over a flat surface. To achieve this, our project employs static optimization code, a powerful computational tool that utilizes mathematical optimization algorithms to estimate muscle forces and activations based on experimental motion data. By comparing muscle activations between unloaded and loaded walking using this approach, we hope to gain valuable insights into the motor control strategies employed by the human body in response to varying loads. Such knowledge may have implications in areas such as rehabilitation, sport performance, and ergonomics, potentially leading to the development of improved training techniques and interventions to enhance human locomotion under different loading conditions.
Research Question(s)
How do differences in ankle, hip, and knee kinematics cause changes in muscle activation patterns during loaded vs unloaded level walking?
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- Carried out Inverse Dynamics using custom static optimization code above.
- The first link under project files above leads to the MATLAB code used for this project. The beginning sections of the code ask for the motion capture files output from Inverse Kinematics. For the unloaded model, the inverse kinematics output came from AddBiomechanics. For the loaded model, the inverse kinematics output came from the Inverse Kinematics tool in OpenSim. After inputting the inverse kinematic files into the MATLAB code, the code is able to conduct inverse dynamics for each model.
- Used static optimization to find muscle activations.
- Part 5 of the MATLAB code attached above conducts static optimization to find the optimal muscle activations necessary in order to minimize activation squared, while still achieving the kinematic and dynamic constraints.
- Validated results with joint kinematics and muscle activations from Dembia et al. paper 1.
- As shown in the results section below, to validate our results from the static optimization code, we compared our results of joint kinematics and muscle activations to those published from Dembia et al.'s work using the same data set [1].
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