A persons gait, is defined as their manner of walking.
For this project we had to find our group members gaits, helping us create an equation (predictive model) to help find peoples height, weight, or even reasons for differences in gait
We started by recording everyones physical data: heigh, weight, leg length etc. After we recorded that in a spreadsheet we used an app called Physics Toolbox Accelerometer. This app would be strapped onto a persons center of gravity (belly button) and would record G-Force vs. Time (s) and Acceleration (m/s/s) vs. Time (s) in x, y, and/or z dimensions. We then collected data for how long it takes to do 10 steps, one on a fixed distance, the other was free ranged.
For this project we had to find our group members gaits, helping us create an equation (predictive model) to help find peoples height, weight, or even reasons for differences in gait
We started by recording everyones physical data: heigh, weight, leg length etc. After we recorded that in a spreadsheet we used an app called Physics Toolbox Accelerometer. This app would be strapped onto a persons center of gravity (belly button) and would record G-Force vs. Time (s) and Acceleration (m/s/s) vs. Time (s) in x, y, and/or z dimensions. We then collected data for how long it takes to do 10 steps, one on a fixed distance, the other was free ranged.
gait_analysis__-_sheet1.pdf | |
File Size: | 59 kb |
File Type: |
What we came up with, we could predict someone's height by seeing the length of their stride and the time they took to walk. The longer the stride and the less time they took to walk the distance, the taller they most likely are.
Terms:
Gait: a persons/subjects manner of walking.
Gait Analysis: an observation and overview of a persons/subjects manner of walking.
Predictive model: equation that uses input data to accurately predict an outcome.
Accelerometer: a device used to measure acceleration.
Gait: a persons/subjects manner of walking.
Gait Analysis: an observation and overview of a persons/subjects manner of walking.
Predictive model: equation that uses input data to accurately predict an outcome.
Accelerometer: a device used to measure acceleration.
Reflection
Overall this project was quick and easy. This project felt like something to keep in our head for further tasks, since we had so much other stuff to balance. Not only with the OP-51 and the capstone, but also trying to stay in touch with our mentors. I think some of the stuff that could've been improved was: the amount of testing, we kind of rushed a lot of our tests and end up having to re do some of them, another could be is putting more time into this project to make it the best it could, and another minor thing we could have changed was communicating outside of school on who was going to work on what. A couple of things we did good on, was actually using our results to help make a predictive model that does work almost 99% of the time, second although we took a lot of data, it made sense. And I think that is one of the most important things if the data makes sense, especially in a situation like this. One big takeaway I got from this project, was that everyone has different gait. Some may have certain disabilities that effect them from achieving certain gaits, and some may just naturally be born with other gates. Overall though I thought that was a cool project we did and definitely something fun to do.
Overall this project was quick and easy. This project felt like something to keep in our head for further tasks, since we had so much other stuff to balance. Not only with the OP-51 and the capstone, but also trying to stay in touch with our mentors. I think some of the stuff that could've been improved was: the amount of testing, we kind of rushed a lot of our tests and end up having to re do some of them, another could be is putting more time into this project to make it the best it could, and another minor thing we could have changed was communicating outside of school on who was going to work on what. A couple of things we did good on, was actually using our results to help make a predictive model that does work almost 99% of the time, second although we took a lot of data, it made sense. And I think that is one of the most important things if the data makes sense, especially in a situation like this. One big takeaway I got from this project, was that everyone has different gait. Some may have certain disabilities that effect them from achieving certain gaits, and some may just naturally be born with other gates. Overall though I thought that was a cool project we did and definitely something fun to do.