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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Another limitation of our analysis is that, because of the data we had access to, we could not use the "matched" trials of loaded walking. Instead, we used the "free" trials for both loaded unloaded walking, meaning the walking speed was different between the loading conditions. This makes it a bit more difficult to compare the results. For the subject/trial we used, the subject walked at 1.16 m/s for the unloaded trial and walked at 1.08 m/s for the loaded trial. Since the subject walked (understandably) slower when wearing the backpack, then the differences in joint moments and muscle activations from the unloaded trial may have been less drastic than if the unloaded speed had been matched.
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Future Work
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For the loaded walking trials conducted in Dembia et al. [1], the subject carried a backpack (8 kg) and 3 weight vests containing lead (30 kg). The backpack worn by the subjects did not have a hip belt [1]. In the OpenSim model, the total load was modeled as a hollow cylindrical channel, with uniform density, welded to the torso [1].
In the future, we would aim to explore the effects of representing the load as a solid cylinder welded to the torso. Furthermore, in the future we seek to examine the influence of redistributing a portion of the load by welding it to the shoulders, as well as attaching additional weight to the back and front of the body. Simulating these different load configurations, would allow us to closely replicate the strain exerted on the body by backpack straps and weight vests. This endeavor promises to provide valuable insights into the resulting joint kinematics and muscle activation patterns, contributing to a more accurate understanding of the biomechanical response to external loads during flat surface walking.
In addition to exploring the effects of load modeling on joint kinematics and muscle activations, it would be interesting to investigate the influence of load magnitude on gait biomechanics. By systematically varying the weight of the load and assessing its impact on joint kinematics and muscle activations, we can gain a comprehensive understanding of how load magnitude interacts with load placement. Furthermore, valuable insights are to be gained through the investigation of the temporal aspects of load carriage on gait biomechanics, examining how load duration and intermittent loading patterns may affect the body's response during walking.
Lastly, only flat surface walking was investigated throughout this project. Future research may focus on how loaded vs unloaded walking gait mechanics change while walking up or down an incline.
Acknowledgments
Big thank you to the ME485 teaching team: Scott Delp, Reed Gurchiek, Jon Stingel, Nicos Haralabidis, Nick Bianco, and Carmichael Ong. Without them, this project would not have been possible.
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