Gait Analysis

Gait Analysis!

For this project, we had to create a predictive model that could be used to estimate a certain value based on the data we collected and the correlations we found. In order to create these charts and models, we gathered walking data from 10 different subjects. These were voluntary participants with heights ranging from 60.5 inches to 75 inches. We focused on their average step distance and height, as well as their average total g-force and height. We put them in order from shortest to tallest, which is how we found the relationship between height and step length, and height and total g-force.

Using this data, we made two normal line graphs to better show these correlations. Then with our understanding of this data, we created a formula that can accurately predict the height of someone based on their stride length, and another one that can accurately predict the height of someone based on their average total g-force during those 5 steps. In order to do this, we made different ranges of step distances and g-force values to predict a range of heights that one might fall in depending on their step length and average g-force. Our formula and data table are precise models that can clearly guess what someone’s height may be. Two graphs were created that show these specific data bins, so that you can tell which bin you fall in.

Content:

• gait: the stride of a human as they move their limbs. we studied people's gaits as they walked 5 steps, in order to see patterns and make correlations with the data.

• gait analysis: gait analysis is the systematic study of human motion, using visual observations and movement measurements for the purpose of helping people with conditions affecting their ability to walk. this is what the project was based on.

• engineering analysis: the application of scientific and analytic principles to reveal the state and properties of a system. this helps engineers begin to understand how and why systems behave the way they do.

• acceleration: the rate of change of velocity. in this case, we used an app to measure acceleration and g-force and then used that data to compare the different gaits of our walking subjects.

• accelerometer: a device that measures the physical acceleration experienced by an object. we used an accelerometer to measure g-force as a person walked.

• g-force: g-force is a measure of acceleration. it is the force of gravity or acceleration on a body. we measured people's average total g-force as they walked 5 steps, and then compared this value with each of their heights to find a correlation.

• predictive model: a model or formula that is able to accurately predict something based on the data and correlation found previously. models allow complex systems to be understood and their behavior predicted. we made predictive models based on the data we got from 10 different subjects.

• symmetry: in terms of gait analysis, the quantification of differences between left-foot and right-foot steps. we studied the symmetry of the way people walked.

• variability: in terms of gait analysis, the quantification of fluctuations from one stride to the next. we studied the variability in each subject's gait.

• velocity: the speed of something that is moving in one direction. in this case, it was how fast each of our 10 subjects walked during the 5 steps.

• dynamicity: in terms of gait analysis, the quantification of variations in kinematic or kinetic parameters within a step.

Reflection:

The Gait Analysis was a very long, detailed, and creative project that made my team and I work really hard for the past few weeks. We did a very good job and finished everything in time, but there were a few things that we could have done better. For instance, it took us awhile to understand what the data from the accelerometer app meant. We finally found out that it was measures of g-force, but we weren't sure what that was until we looked it up. Another thing that took us some time to decide was what to do with the outliers in our data because there was no correlation that we could find when they were present. Once we took them out, we ended up finding a relationship between height and total g-force and height and average step length, right away.

Regarding my personal work ethic throughout this project, I would say that I did the best that I've done so far this year. Two of the six C's that I excelled in include conscientious learning and communication. I managed my time very well during the past few weeks, and I never turned anything in late. This goes for my team as well. We always finished each assignment before it was due and it was always done thoroughly and nicely. We also did a very good job communicating throughout the project. One of our group members wasn't here for a few days, so we made sure to fill her in on what she missed and explained things to her if she was confused. We texted each other and talked in class to make sure we were always on the same page as one another and had the same thoughts/opinions on a topic/problem.

However, there were two C's that my group and I could have done better in, and those include critical thinking and maybe creativity. We could have thought a bit more critically about some things when we got stuck. For example, we didn't know how to add g-force into our data and predictive models, so we looked at examples of past classmates to help us and guide us to the correct answer. Instead of looking for help from others, we could have thought a bit more and have come up with our own ideas to fix this problem that stumped us. Creativity is another category that we could have done a bit better in. All of our work was done really well and we used our creative minds to do it, but there wasn't much drawing or art in this project, so creativity didn't play a huge role. Next project, however, we will make sure to focus on being more creative and critically thinking more!