Introduction
Gait is described as a person’s specific manner of walking. Everyone walks differently, with many distinctive motions they tend to do while walking. Various factors in their life such as past injury, physical disability, and many other factors make individuals walk in that specific way. Some main differences in the way people walk are due to the pressure on each foot when stepping and the posture in which they stand when they are walking. Individuals with different heights and weights also move differently as these factors help dictate the pressure and stress placed on joints.
Sometimes, we can recognize certain people by the way they move or even just hearing them walk. When you get really familiar with a person, you can tell the difference between them walking and other people walking just by the sound of their steps. This proves that gait analysis is both visual and auditory.
Differences in gaits can help identify people because the brain can associate a certain person with the way their walk looks or sounds.
There are many things that are noticeable when a person walks. Some of the initial things are their overall posture, stride length, hinge in the hip, and knee angle. Each of these things are different for each person. That is why law enforcement uses Gait technology at times.
Gait testing provides tons of data to be analyzed. By taking video and downloading an app to help diagnose data we can collect, the speed, hip angle, chest height, hip height, and many other things would be useful data . This data can then be plotted on a graph or data table that will make it more clear to the readers diagnosing the data.Beyond this general data, there is also specific leg data to be collected. We can collect data about the speed, movement, and flexibility of each leg, collected from joints and flexibility of certain parts on the leg. This will then be able to be processed and graphed for viewing.
Below you can see the progress of our work and the presentation that we used to help us find a predictive model and for the gait of each person. You can also see our videos and other things we used in this project.
Sometimes, we can recognize certain people by the way they move or even just hearing them walk. When you get really familiar with a person, you can tell the difference between them walking and other people walking just by the sound of their steps. This proves that gait analysis is both visual and auditory.
Differences in gaits can help identify people because the brain can associate a certain person with the way their walk looks or sounds.
There are many things that are noticeable when a person walks. Some of the initial things are their overall posture, stride length, hinge in the hip, and knee angle. Each of these things are different for each person. That is why law enforcement uses Gait technology at times.
Gait testing provides tons of data to be analyzed. By taking video and downloading an app to help diagnose data we can collect, the speed, hip angle, chest height, hip height, and many other things would be useful data . This data can then be plotted on a graph or data table that will make it more clear to the readers diagnosing the data.Beyond this general data, there is also specific leg data to be collected. We can collect data about the speed, movement, and flexibility of each leg, collected from joints and flexibility of certain parts on the leg. This will then be able to be processed and graphed for viewing.
Below you can see the progress of our work and the presentation that we used to help us find a predictive model and for the gait of each person. You can also see our videos and other things we used in this project.
Content
Above you can see the data we collected in our work on this project through the many weeks. You can also see the graphs and visuals we presented in our presentation.
The data that we collected showed many things about our subjects and how they walked. The three different subjects had three very different heights and three different leg lengths which we found were the two most crucial elements to determining patterns in movement and an individual's gait. Even with such a small sample size we can see the patterns that have developed in the data. As seen in the horizontal movement graph, the larger the value is, the smaller in height the person should be. This can be seen with the Thomas (the shortest person) who had the largest median numbers on the horizontal movement graph. With more time we could have figured out why this was or had a larger sample size but we were very limited on time due to the airplane training and other products.
This data also shows a correlation in leg length and the absolute value vs. time graph. The leg length creates a larger range in the absolute value maximum and minimum values. This can be seen with Josh (the person with the longest legs) who had the largest range of absolute value values in the graph. This large range in absolute values in the graph show a broad swing in stride length. It also shows overcompensation on the right leg than the left leg. This is due to Josh’s injury that creates an abnormal gait than many people have. This creates changes in all three of the graphs but specifically absolute value.
Horizontal distance is the greatest in Matthew’s gait graph. This is confusing and there is no correlation between his leg or hip height and his stride length. It could have been because of a bigger bounce in his step but more data would have to be collected to tell if this is true or not. Matt had the most average measurements among all three people yet he had the longest stride length which would be an interesting topic to research in the future.
Finally, the data and results we found from our experiment produced a model and equation that correlated between the height and weight of a person and the g-force acting on the person at the time. The equation can be seen on the presentation that we turned in along with this presentation. The presentation focuses on height, stride length, and g-force, to create a model that takes the average to find the predicted g-force. The model was very accurate at predicting this g-force as we predicted that the g-forces would range from 1-3g’s and this turned out to be true when we tested it. The model was accurate and we were able to predict g-forces accurately.
The data that we collected showed many things about our subjects and how they walked. The three different subjects had three very different heights and three different leg lengths which we found were the two most crucial elements to determining patterns in movement and an individual's gait. Even with such a small sample size we can see the patterns that have developed in the data. As seen in the horizontal movement graph, the larger the value is, the smaller in height the person should be. This can be seen with the Thomas (the shortest person) who had the largest median numbers on the horizontal movement graph. With more time we could have figured out why this was or had a larger sample size but we were very limited on time due to the airplane training and other products.
This data also shows a correlation in leg length and the absolute value vs. time graph. The leg length creates a larger range in the absolute value maximum and minimum values. This can be seen with Josh (the person with the longest legs) who had the largest range of absolute value values in the graph. This large range in absolute values in the graph show a broad swing in stride length. It also shows overcompensation on the right leg than the left leg. This is due to Josh’s injury that creates an abnormal gait than many people have. This creates changes in all three of the graphs but specifically absolute value.
Horizontal distance is the greatest in Matthew’s gait graph. This is confusing and there is no correlation between his leg or hip height and his stride length. It could have been because of a bigger bounce in his step but more data would have to be collected to tell if this is true or not. Matt had the most average measurements among all three people yet he had the longest stride length which would be an interesting topic to research in the future.
Finally, the data and results we found from our experiment produced a model and equation that correlated between the height and weight of a person and the g-force acting on the person at the time. The equation can be seen on the presentation that we turned in along with this presentation. The presentation focuses on height, stride length, and g-force, to create a model that takes the average to find the predicted g-force. The model was very accurate at predicting this g-force as we predicted that the g-forces would range from 1-3g’s and this turned out to be true when we tested it. The model was accurate and we were able to predict g-forces accurately.
Reflections
In this experiment there were both positives and negatives that will benefit me in future projects and teamwork activities inside and out of the classroom. One positive was the teamwork and communication that we used even through the different projects and deadlines that were due during this time. This array of things that were going on made it hard to focus solely on the project and made us turn our attention to a variety of different things. Another positive that came from this was our improved self discipline ability. There was a lot of time that could have been spent off task and we had to hold each other accountable. This self discipline and work on communication only strengthened our team so that we could produce a quality, finished project that was turned in on time.
There were also some negatives that came from this project. One would be lack of planning at the beginning of the project. It would have been better if we took time to plan out our action plan from the start but instead we rushed into the project with a series of trial and error style attempts that took more time. Another negative that we can learn from is the use of the tools given to us. In this project we should have explored the app for more time to get more familiar with it before conducting trials so that we could get more accurate and even data.
There were also some negatives that came from this project. One would be lack of planning at the beginning of the project. It would have been better if we took time to plan out our action plan from the start but instead we rushed into the project with a series of trial and error style attempts that took more time. Another negative that we can learn from is the use of the tools given to us. In this project we should have explored the app for more time to get more familiar with it before conducting trials so that we could get more accurate and even data.