Project Overview Have you ever wondered why people walk differently? Well, it’s determined by your gait, which is “a person’s manner of walking.” In this project we were able to decipher this question. Using the app "Physics Toolbox Accelerometer" by Vieyra Software, we tested things such as the difference between acceleration and time, of three test subjects, varying in age, weight, height, and sex. The other factors we recorded were which foot the subjects stepped with first, how fast they were walking, how the phone placement, and how many steps were taken. In this project we were able to determine and understand the relationship between height and gait frequency for walking humans. The main purpose of the project was to introduce us to spreadsheets and how to analyze the data from these sheets. We began this project by looking at a set of data that gave us 8 subjects, 4 of them were children and 4 were adults. We were then asked to look at a new set of data and using what we had just learned, label each new subject as either a child or adult. We guessed that subjects 1 and 3 were the children because there were 3 main fluctuation in the data. In subjects 1 and 3 there was a much longer step time, a much higher standard deviation in acceleration in the vertical axis, and a higher standard deviation in acceleration in the anterior axis. We guessed that these factors allowed us to reasonably assume that the subjects 1 and 3 were the children because the data coincided with the children on the other data sheet. The next step of the project was to download an app that would allow us to measure acceleration on 3 different axis's. We were tasked to explain how our data could be used to hypothesize a person’s height. The resulting equation, from the analysis, was strides/time = frequency. Then using the frequency, we determined you can predict the height by analyzing the trend-line from our data
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 accelerometor 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.
Conclusion Our results were that the taller a person is, the shorter their frequency is. Our data supported this. We were able to create a graph and equation based on our data to predict height. If we redid this project we would probably have more test subjects so that we would have more data. This project was intriguing because now we are able to predict height based on gait. Learning other ways gait can predict height would also be interesting. Some of our best moments were on the first day we were able to grind out all of the experiments we would need for the entire project. Next, the best moment was when we finally hit the breakthrough on how to get an equation for our graphs. This was the most difficult part of the project. Our lowest points were when we would run out of steam and get lazy. there were a few times where we wouldn't do what we needed to get done. Overall it was a grueling process with very constrained time limits but with hard work we were able to get it all done.