Gait Analysis
Introduction
In this project, we were tasked with gathering data on how our team members walked, and using that data to create a predictive model used to predict something relative to their walking, such as height, speed, weight, etc. Our group took measurements of our team members, leg length and overall height, and then asked them to walk 8m, in hopes to collect some data that would reveal a pattern, leading us to create a mathematical predictive model.
Content
In order to create our predictive model, we decidede to take data first and then use the data to find a pattern. The experiment we conducted was essentially just counting the number of steps taken in a given distance. We left the accelerometer on our test subject so as to collect that data as well, we just did not end up using it. We created an 8m path and asked our subjects to walk as normal as they could, so we could collect data, the data is as follows.
Unfortunately a pattern did not reveal itself, so we all thought of how we could use these values to create an equation. We have a height, the leg length, and a distance and the only mathematical concepts I could think of that use those two values to predict something, trigonometry. Using our predictive model, we can use the leg length of a given person, as well as the number of steps they take in a given distance, to not only predict the length of their stride, but the angle their legs form during a step once their heel hits the ground. In order to create this equation, we used the data from Mi’Zauni’s steps. First we divided the total distance traveled, 8m, by her average number of steps, approximately 12, to get an average stride length of .66m. When a person walks, generally their legs form an equilateral triangle, so my stride distance of .66m, represents the distance from foot to foot. However, in order to use trigonometry to solve, we must create a right angle, cutting the equilateral triangle in half and therefore cutting my stride distance in half from .66m to .33m. Using the .33m, we can use trigonometry to find what would be one half of the angle formed by my legs, because we cut the distance in half. The equation we used was sinθ=.33m/.83m. When we solve this equation, we get a value of 23 degrees for the angle made at the base of my legs. We then multiply it by two to get an actual angle of 46 degree, where as my actual was 45 degrees. In order to prove this, we used Sydney’s stride length of .72m and height of .98m to create this equation, (sinθ=.36/.98)2 to get an angle of 42 degrees, only 6 degrees off from her actual angle of 48 degrees. Unfortunately, due to the time it took to create the predictive model, we were unable to test the equation on the other members of our experiment. However, the data we did collect shows that our predictive model has a confidence interval of +/- 13%. We believe we can accredit that large interval to human error in the actual angle measurement process.
Vocabulary
Gait - the manner in which someone walks
Accelerometer - a tool that measures proper acceleration
Trigonemetry - mathematics that studies relationships between side lengths and angles of triangles
Confidence Interval - a range of values so defined that there is a specified probability that the value of a parameter lies within it
Reflection
The entire purpose of this project was simply for us to collaborate with our team to determine if this is the group we want to work on our Capstone Project with. This project was intended to emphasize and foster communication and collaboration skills with your group, something my group was somewhat able to do. One of my group members is not to knowledgable about most of the things we are doing, but with a few simple explanations, he is able to work independently an contribute in any way he can, making him a great asset to our future group. Next, I had a group member, similar to the first, however much more needy. He felt as though not only did he need a detailed step by step explanation of any assignment or task he was given, but he felt that he needed permission to work on anything collaboratively. This slowed us down tremendously because he would question everything we did, simply out of pure curiousity, and unfortunately, we were forced to leave him in the dark on many topics in order to finish our work. It felt unfair, but in order for us to be able to complete a task, we did not have expendible time to spend going over every detail. Lastly, I had a group member similar to me, one who works independently and then shares her findings afterwards. However, she collaborated with another group resulting in a questionable predictive model; following slight debate, we decided to follow my model and use that equation and it's findings. Overall, I believe the team worked well for me, however, other people, such as member 2, might find that this was not a productive environment for them, as we did not cater to everyone's specific learning style. Collaboration throughout the project was clear, as we tried our best to work together and generally completed assignments together and on time. However, communication was subpar, with very little ides being shared until they were fully realized and already put into practice. Group members were more often told what to do, rather than how to do it.