Project Description
For this Gait Analysis project, we were tasked with running trials and finding trends in our data using Google Sheets to analyze the data. The question that we asked ourselves when doing this project was: how can we relate gait frequency and the height of a person? To answer this question, we first took characteristic data about the people who we tested. For example, we measured height, leg length, step time, step length, etc. We then did some data trials with accelerometers on our phones, which allowed us to plot the cyclical function of steps. The accelerometers measured the G-force, which is acceleration based on the ratio of the normal force to the gravitational force. The graphs we created can be seen in the report below, and they show the cyclical repetition that occurs when someone walks.
To create our report, we assigned each group member a separate part to write up and then we me to compile our findings and create the one final report you can find below. Overall, we spent 1 day running trials, and the rest of our time (classes and at home over a 2 week period) doing additional research online, analyzing our data with graphs, and writing a report of our findings.
We presented our findings with a Google Slides presentation, and we also turned in a written report, both of which can be found below.
To create our report, we assigned each group member a separate part to write up and then we me to compile our findings and create the one final report you can find below. Overall, we spent 1 day running trials, and the rest of our time (classes and at home over a 2 week period) doing additional research online, analyzing our data with graphs, and writing a report of our findings.
We presented our findings with a Google Slides presentation, and we also turned in a written report, both of which can be found below.
Concepts
Gait analysis - study of animal locomotion, more specifically the study of human motion, using the eye and the brain of observers, augmented by instrumentation for measuring body movements, body mechanics, and the activity of the muscles. (Taken from this Wikipedia page, see for more information)
Predictive modeling - process that uses statistics/data to predict future outcomes, In this project, our predictive model was an equation
Gait - a person's manner of walking
Vertical - up and down direction
Lateral - side to side direction
Anterior - front and back direction
Acceleration - change in velocity over time, measured with accelerometer
G-force - an acceleration, ratio of the normal force over the gravitational force (9.8 m/s^2)
Frequency - waves per unit of time, typically seconds (Hz)
Many facets of this project relate to other disciplines. For example, using the data uses a lot of statistics and math to analyze the data. Then, we also need to have a visual component to present our data, which takes artistic talent. Another area that connects with this engineering project is physics, which helped us explain the cyclical nature of the gaits. We could use physics equations such as frequency = 1/period to calculate times for our steps, and we also knew how to read the G-force data because of our understanding of gravity and normal force. Lastly, we used a lot of English skills and concepts to write our report after we finished our analysis.
Predictive modeling - process that uses statistics/data to predict future outcomes, In this project, our predictive model was an equation
Gait - a person's manner of walking
Vertical - up and down direction
Lateral - side to side direction
Anterior - front and back direction
Acceleration - change in velocity over time, measured with accelerometer
G-force - an acceleration, ratio of the normal force over the gravitational force (9.8 m/s^2)
Frequency - waves per unit of time, typically seconds (Hz)
Many facets of this project relate to other disciplines. For example, using the data uses a lot of statistics and math to analyze the data. Then, we also need to have a visual component to present our data, which takes artistic talent. Another area that connects with this engineering project is physics, which helped us explain the cyclical nature of the gaits. We could use physics equations such as frequency = 1/period to calculate times for our steps, and we also knew how to read the G-force data because of our understanding of gravity and normal force. Lastly, we used a lot of English skills and concepts to write our report after we finished our analysis.
Reflection
This project interested me because it gave us lots of experience with analyzing and manipulating data in Google Sheets. One peak, something that I improved on, during this project was data manipulation to create graphs for analyzing trends. We were given raw data from the accelerometer that was unreadable in the sense of finding trends. I learned to use Google Sheets to create the graphs that helped us see the cyclical pattern of gaits. Another peak, something I also improved on, was data collection and collaborating with other people to get that data. We had different people collect different parts of the data in order to maximize our efficiency, so we all had to rely on being organized and labeling our data so other people could use it. However, there were also parts that we could've improved on. One pit that we could have improved on was time management towards the beginning of the project. We did a lot of the work on this project in the last few days, so it would've been better if we had broken down parts more in our gantt chart to set smaller goals for sooner dates. Another pit was our quality of data. If I were to do this project again, I would probably want tests from a more diverse group of people. Both of our data sets were from girls (Alexa and Michelle) of about the same age, height, leg length, and step length, so the data was not easy to see drastic trends in. However, we did add more data from a taller person (me) that confirmed our predictive model, so overall I think our data and hypothesis is still reliable.