Team Members
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Unloaded GRF XML file subject 09 trial 04
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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- Scaled OpenSim models in AddBiomechanics.
- In Dembia et al. [1], the researchers manually scaled the model. AddBiomechanics is a new software that has the ability to replace labor-intensive manual scaling and marker registration. To see if the marker errors would be reduced compared to manual scaling, in this project, we looked to scale the unloaded and loaded models utilizing AddBiomechanics. Scaling the unloaded model in AddBiomechanics resulted in reduced marker eerrors. However, we were unable to scale the loaded model in AddBiomechanics because the software continuously looked to reduce the load of the backpack that we wanted to keep constant on the loaded model. Therefore, we used the manually scaled loaded model utilized in Dembia et al. [1].
- Carried out Inverse Kinematics and Inverse Dynamics using custom static optimization code above.
- Used static optimization to find muscle activations.
- Validated results with joint kinematics and muscle activations from Dembia et al. paper 1.
Results
Joint Angles
Joint Moments
Muscle Activations
Glut Med Activation
Limitations
- rigid tendon instead of elastic tendon
- couldn't use matched speed for loaded, so had to use free speed (1.16m/s for unloaded, 1.08m/s for loaded)
- stitching due to walking trials being "out of order"
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