Gait Analysis

Gait Analysis Project Procedure

The purpose of the Gait Analysis Project was to find a relationship between someone’s gait and a trait of the person being analyzed. We began by finding gFx, gFy, and gFz, using the "Physics Toolbox Accelerometer", of each group member’s gait. Each person did three trials. We put all of the information gathered into separate tables for each person. We found the average of each of the three trials for each person, and from the average tables, we each produced charts that represent our average gait. These charts included averages of gFx, gFy, and gFz. After comparing our groups charts, we noticed that the range of each group member’s gFx was different. We also noticed that the taller members had larger ranges, and the shorter members had smaller ranges. This information inspired our prediction model. We made a new table that only had height vs range of gFx. Our new chart produced from this table showed a strong relationship, confirming our hypothesis. After adding a trend line and a line of best fit to our chart, we were able to find an equation to predict the relationship between height and gFx. The equation we found was y=0.08x-4.77. The y value is a person's range of gFx, and the x value is the height of the person. By inserting the value of either of these components, you can solve for the other value. We tested our equation with multiple peers, and our equation was pretty accurate with only a small margin of error.

Evidence of Work

Final Report

To the right is our final report that includes our gait analysis project report, charts and predictive model. All components are explained and visualized so that the viewer is able to fully understand our ideas.

Gait Analysis Project Report, Charts and Predictive model

Gait Analysis Presentation

Slide Show Presentation

This is the presentation our group used to present our research and gait analysis to the class. In this presentation we condensed the information to the most concise explanation possible so that our audience wouldn't get distracted, and had enough information to fully understand our thought process.

Content

Gait: a pattern of limb movements made during locomotion. Human gaits are the various ways in which a human can move, either naturally or as a result of specialized training.
Acceleration: rate of change of velocity. In this project, we used an app to measure acceleration and then created graphs to compare our subjects data.
Accelerometer: a device that detects acceleration and tilt. We used the accelerometer in our app measuring.
G-force: g-force is a measure of acceleration.
G-force Meter: a g-Force meter is able to measure the ratio of normal force to gravitational force, in three dimensions.
X Direction: lateral axis measuring movement from right to left.
Y Direction: vertical axis measuring movement up and down.
Z Direction: axis measuring movement forwards and backwards.

Velocity: the speed of something that is moving in one direction. In this project, it was how fast our subjects walked.
Margin Of Error: an amount (usually small) that is allowed for in case of miscalculation or change of circumstances.
Dynamicity: in terms of gait analysis, the quantification of variations in kinematic or kinetic parameters within a step.
Extrapolation: the action of estimating or concluding something by assuming that existing trends will continue or a current method will remain applicable.
Metric: a quantitative indicator of a characteristic or attribute.
Model: in technology, a description of observed or predicted behavior of some system, simplified by ignoring certain details. Models allow complex systems to be understood and their behavior predicted.
Symmetry: in terms of gait analysis, the quantification of differences between left-foot and right-foot steps.
Variability: in terms of gait analysis, the quantification of fluctuations from one stride to the next.

Reflection

The Gait Analysis project was the firtst project that our capstone group did together. It was an important project because we were able to see how we each worked together, and figure out eachother's various strengths and weaknesses. Luckily for us, our group worked really well together in order to make sure all tasks got done. I think we all bring different strengths to the group, and this makes us a very well- rounded group. We are also all very supportive of eachother, so we are able to support eachother through our weaknesses. In this project, I believe I demonstrated good critical thinking skills, as the analysis piece of this project was very in depth and required some deeper thinking. Finding a fitting equation that could explain our data was challenging, but we never gave up. I also believe my collaboration was strongly exhibited, as the group had to work well together in order to accomplish our goals for the project. This included good time management, as we worked hard in class every day, and even distrubution of tasks.

While these aspects of the project were great, I do feel that there were some portions of the project that I could have done better on, and will work on improving in future projects. Primarily, my communication could have been improved while working with teammates, as we each prioritized different parts of the project. This stressed me out as I felt that important parts of the prject were being procrastinated, so I just went in and did the work by myself. I feel that in future work with my group I will need to work on expressing my concerns more clearly. Additionally, I felt my conscientious learning was lacking, as I was not fully aware of how the project was applicable to the capstone project. Overall, I enjoyed the project and the process of thinking through how to build a predictive model and equation.

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