In this project, we were trying to answer the question "how does height affect a person's gait?" To answer this question, we downloaded an app called Physics Toolbox Accelerometer, which measured gravitational force in the x, y, and z directions while a person is walking. Each of the four members of our group taped the phone to their chest with the app running and performed three trials. We also recorded the time it took for each trial, as well as each person's stride length, height, steps taken per trial, and starter foot. We used this data in combination with the data from the app to create a model that would predict a person's height range based on their gait data.
The final product was a detailed lab report and a micro-presentation that showed the highlights of the report. The lab report includes graphs of our data, a predictive model, and explanation of how the model works and its effectiveness. We found that the taller a person us, the lower gait frequency they have. From this discovery, we were able to create a model that classified people as short or tall based on their average gait frequency.
The lab report and micro-presentation can be found below.
The final product was a detailed lab report and a micro-presentation that showed the highlights of the report. The lab report includes graphs of our data, a predictive model, and explanation of how the model works and its effectiveness. We found that the taller a person us, the lower gait frequency they have. From this discovery, we were able to create a model that classified people as short or tall based on their average gait frequency.
The lab report and micro-presentation can be found below.
Key Concepts
All key terms are defined as they relate to gait.
Accelerometer: A device that measures the physical acceleration experienced by an object. We used an accelerometer to take our gait data, allowing us to answer our driving question. Accelerometers are used in many other disciplines. For example, they are used in cars do detect crashes and deploy air bags.
Dynamicity: The quantification of variations in kinematic or kinetic parameters within a step. The graphs in the appendix of our lab report showed dynamicity because they showed each step as a wave, allowing us to look at the structure of each wave and determine variations within each step.
Gait: The stride of a human as they move their limbs. This whole project was about measuring gait. Gait analysis can be used by doctors to determine treatment or forensic analysts to identify people from their strides.
Metric: A quantitative indicator of a characteristic or attribute. This project made use of many metrics, including gFx, gFy, and gFz. Metrics are used in disciplines that involve lots of data, such as scientific research.
Model: 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. The end goal of this project was a model that would be used to predict someone's height based on their gait data.
Symmetry: The quantification of differences between left-foot and right-foot steps. You can determine symmetry from our graphs by looking at every second wave as one foot's stride and the other waves as the other foot's stride, then comparing the two sets.
Variability: The quantification of fluctuations from one stride to the next. Variability can be seen in our graphs by comparing the sizes of each wave.
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.
All key terms are defined as they relate to gait.
Accelerometer: A device that measures the physical acceleration experienced by an object. We used an accelerometer to take our gait data, allowing us to answer our driving question. Accelerometers are used in many other disciplines. For example, they are used in cars do detect crashes and deploy air bags.
Dynamicity: The quantification of variations in kinematic or kinetic parameters within a step. The graphs in the appendix of our lab report showed dynamicity because they showed each step as a wave, allowing us to look at the structure of each wave and determine variations within each step.
Gait: The stride of a human as they move their limbs. This whole project was about measuring gait. Gait analysis can be used by doctors to determine treatment or forensic analysts to identify people from their strides.
Metric: A quantitative indicator of a characteristic or attribute. This project made use of many metrics, including gFx, gFy, and gFz. Metrics are used in disciplines that involve lots of data, such as scientific research.
Model: 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. The end goal of this project was a model that would be used to predict someone's height based on their gait data.
Symmetry: The quantification of differences between left-foot and right-foot steps. You can determine symmetry from our graphs by looking at every second wave as one foot's stride and the other waves as the other foot's stride, then comparing the two sets.
Variability: The quantification of fluctuations from one stride to the next. Variability can be seen in our graphs by comparing the sizes of each wave.
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.
This project had many positive elements. First, our time management went very well. We were finished with most of our report a few days before it was due, so we were able to thoroughly proofread it and make it the best it could be. This also meant we were less stressed as the due date came closer. Another thing that went very well was our data collection. We did twelve trials total (three per group member), so we had a lot of data to gather. However, we were able to do it efficiently, and it only took us one and a half class periods to collect all the data we needed. Overall, these two peaks helped us complete our project smoothly and effectively.
Although the project went well overall, there were also some pits. First, we did not have a good understanding of what we were doing at the beginning of the project. We knew we were finding the connection between height and gait, but we did not know how to make the connection. Our data was shown as a large list of numbers of a spreadsheet, and no one knew what to make of it. However, we were finally able to make progress when we took the averages of each metric for each trial and compared them to each other. Another thing we struggled with was division of work. I ended up doing most of the project with one other group member. This was likely because of poor communication, so some group members didn't know what their tasks were.
One thing we learned from this project was what an accelerometer is and how it works. I learned that it can measure forces in different directions, and its data can be graphed to show waves that dive a visual representation of a person's gait pattern. Another thing I learned was how to work through something I don't understand and teach myself about it. I couldn't find many helpful articles on the Internet, so I worked with my team to make sense of the data, and we ended up with a working predictive model.
Although the project went well overall, there were also some pits. First, we did not have a good understanding of what we were doing at the beginning of the project. We knew we were finding the connection between height and gait, but we did not know how to make the connection. Our data was shown as a large list of numbers of a spreadsheet, and no one knew what to make of it. However, we were finally able to make progress when we took the averages of each metric for each trial and compared them to each other. Another thing we struggled with was division of work. I ended up doing most of the project with one other group member. This was likely because of poor communication, so some group members didn't know what their tasks were.
One thing we learned from this project was what an accelerometer is and how it works. I learned that it can measure forces in different directions, and its data can be graphed to show waves that dive a visual representation of a person's gait pattern. Another thing I learned was how to work through something I don't understand and teach myself about it. I couldn't find many helpful articles on the Internet, so I worked with my team to make sense of the data, and we ended up with a working predictive model.