This lab style project required my partner and i to first investigate an app used to record body motions while walking. We did this by investigating what the x,y,z, axis were and seeing what moment changed them.
Once the investigation off how to operate the app was done we proceeded to put the phone around our wast and begin tests. We conducted three timed tests, each in the same area but with the phone in different orientation. This allowed us to measure the data differently along each axis and discover the most about GAIT as we could. |
DataMy partner and I took the data off of the app and uploaded it on to a google sheets. We then organized this data a long with all the test parameters.
Here is a link to our LAB |
GraphsAlong with the analog data we collected we also used graphs to compare visual changes that occurred. We noticed that as you oriented the phone differently it individually changed one of the axis. For example as we rotated the phone one of the axis would be graphed in the negative region. This showed us how important phone location is for making predictions about a subject.
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PredictionsOne of the goals of this project was to be able to analyze GAIT data and be able to predict who the person is and if the pattern would continue. We made the predictive model by taking the number of steps (15) and dividing it by the distance walked (609.6 cm). The average step was 40.64cm which was multiplied by the average woman's height ratio to get height of 167.8cm. This is the same height as my partner which confirms that our predictive model was accurate.
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Reflection
This project started off with the exploration of an app which required my partner and I to use our phones separately and come together to make conclusions about our findings. Through this we then determined what was the best way to conduct the rest of our experiment. This helped us have two different opinions on how we should conduct the lab which we joined together to create a procedure that had less human error. After we conducted the lab we moved on to organizing the data and eventually sharing it with the whole classes. The whole class data base helped us see how other students research went and what they might have done wrong so we could all work together to come up with the most accurate predictive models. Throughout this process we were able to learn about GAIT modeling and how to create accurate predictive models. If we were to do this lab again it would be a good idea to do more research on what GAIT modeling is so we have a better understanding of the project before we start it. Overall this project went well and I learned a lot about collaboration and data collection.