Project Description
For this project, we studied the way a human walks by measuring their bodies movements, body mechanics, and activity of the muscles. This is called gait, and from this we can find one's height, weight, age, and leg length. Our driving question was this: What is the relationship between the height and gait frequency of a walking human? We did this in a group of 4 by conducting an experiment on each group member and deducting a lot of data. The data we collected consisted of the distance they walked, how many steps they took, their stride length, their height, their leg length, and their shoe size. Then, we compared this data throughout all 4 members taken by an app called Physics Toolbox Accelerometer. This app helped us gather data on their horizontal, vertical, and forward accelerations. We proceeded to construct graphs of these accelerations, gFx, gFy, and gFy. In conclusion, we determined that the relationship between a person's height and their gait frequency is this: the shorter a person is the larger their gait frequency is. We created a detailed lab report which includes graphs of our data, a predictive model, and an explanation of how the model works and its effectiveness. In addition, my team built a micro-presentation that showed the highlights of the report and a model that classified people as short or tall based on their average gait frequency.
gait_analysis_lab_report.pdf | |
File Size: | 579 kb |
File Type: |
Our lab report.
gait_analysis.pdf | |
File Size: | 94 kb |
File Type: |
Our micro-presentation.
Concepts
Accelerometer: A device that measures the physical acceleration experienced by an object.
Dynamicity: In terms of gait analysis, the qualification of variations in kinematic or kinetic parameters within a step.
Gait: The stride of a human as he/she moves his/her limbs.
Metric: A quantitative indicator of a characteristic or attribute.
Model: In technology, a description of observed or predicted behavior of some system, simplified by ignoring certain details. Models allow complex systems to be understood and their behavior predicted.
Symmetry: In terms of gait analysis, the quantification of differences between left-foot and right-foot steps.
Variability: In terms of gait analysis, the quantification of fluctuations from one stride to the next.
gFx: Average horizontal gravitational force.
gFy: Average vertical gravitational force.
gFz: Average forward gravitational force.
Gait frequency: The amount of steps a person takes in a set distance. Gait frequency was the metric we used in our model, since it was the only one where we saw a clear difference based on the person's height.
Dynamicity: In terms of gait analysis, the qualification of variations in kinematic or kinetic parameters within a step.
Gait: The stride of a human as he/she moves his/her limbs.
Metric: A quantitative indicator of a characteristic or attribute.
Model: In technology, a description of observed or predicted behavior of some system, simplified by ignoring certain details. Models allow complex systems to be understood and their behavior predicted.
Symmetry: In terms of gait analysis, the quantification of differences between left-foot and right-foot steps.
Variability: In terms of gait analysis, the quantification of fluctuations from one stride to the next.
gFx: Average horizontal gravitational force.
gFy: Average vertical gravitational force.
gFz: Average forward gravitational force.
Gait frequency: The amount of steps a person takes in a set distance. Gait frequency was the metric we used in our model, since it was the only one where we saw a clear difference based on the person's height.
Reflection
This project provided practice in a field that I had not been involved in in awhile. This is the practice of gathering data and making means of it. Our group did this very well. We collected data on many things that could possibly play a part in our results. In addition, we produced 3 trials for each person for a total of 12, which gave us clear data. We did this in depth while also being efficient, a combination that takes difficulty to achieve. Another thing that went well was our communication, which led to quality time management. Each member knew their specific job and how it would come together in the end. We supported each other through the data collection process and kept the morale at a high. We collaborated consistently, and we talked through issues and needed completions for deadlines. This helped my understanding of the complex topic because I had support and reliable friends I could turn to for any assistance. All of us pitched in for extra help which led us to complete the assignment before the deadline, thus giving us time to refine our final product.
Although the project process went nice and smooth, we did have some hiccups along the way. First, the problem with all equal contribution and collaboration is that we did not have a leader. A leader could have given us direction. In a way, we all led each other and ourselves, however a specific team leader who is assertive and well-rounded is always beneficial in a lab or analytical setting. Second, I was absent due to a medical issue for a solid part of the time we were working on the micro-presentation, and my group members did not reach out and give me much work to do. Of course, I was still recovering and wouldn't have been able to do it as cleanly as usual. However, it would have benefited my group and I if I knew what was going on and how to help.
I learned a few things about myself through this project, First, I realized what my favorite part of a lab is: the data collection. Even though I don't know the results or if my hypothesis is correct, the process of relaying data in a team setting excites me. I appreciate the preciseness needed for quality data and the room for human error. Second, I learned to be more grateful and mindful of my limbs and each of their specific functions. They all work together to allow me to move, and they offer me the privilege to do basic things like walk and run that not everyone can do.
Although the project process went nice and smooth, we did have some hiccups along the way. First, the problem with all equal contribution and collaboration is that we did not have a leader. A leader could have given us direction. In a way, we all led each other and ourselves, however a specific team leader who is assertive and well-rounded is always beneficial in a lab or analytical setting. Second, I was absent due to a medical issue for a solid part of the time we were working on the micro-presentation, and my group members did not reach out and give me much work to do. Of course, I was still recovering and wouldn't have been able to do it as cleanly as usual. However, it would have benefited my group and I if I knew what was going on and how to help.
I learned a few things about myself through this project, First, I realized what my favorite part of a lab is: the data collection. Even though I don't know the results or if my hypothesis is correct, the process of relaying data in a team setting excites me. I appreciate the preciseness needed for quality data and the room for human error. Second, I learned to be more grateful and mindful of my limbs and each of their specific functions. They all work together to allow me to move, and they offer me the privilege to do basic things like walk and run that not everyone can do.