Background:
For the experiment we measured someone's gait and analyzed the data we received from using an accelerometer app. We then input the data into Google Sheets and made predictive models.
Link to Data and Charts
For the experiment we measured someone's gait and analyzed the data we received from using an accelerometer app. We then input the data into Google Sheets and made predictive models.
Link to Data and Charts
Terms to Know
accelerometer: a device that measures the physical acceleration experienced by an object
gait: the stride of a human as they move
metric: a quantitative indicator of a characteristic or attribute
model: a description of observed or predicted behavior of some system, allows complex systems to be understood and their behavior predicted
accelerometer: a device that measures the physical acceleration experienced by an object
gait: the stride of a human as they move
metric: a quantitative indicator of a characteristic or attribute
model: a description of observed or predicted behavior of some system, allows complex systems to be understood and their behavior predicted
Reflection
Challenges I encountered while working on the Whole-Class Database was what data to use and then how and what format to graph the data in. The "quality of teaming" was good and we both worked equally and solved problems together. My team collaborated and communicated with other teams when we had a question and asked if they could help us with it. We also asked clarifying questions. We didn't have problems with the data we recorded. We were able to make sufficient graphs and interpret the data. We didn't notice any shortcomings. To design a better experiment, we could do further research into the subject beforehand to make sure we all thoroughly understand the subject matter. Challenges while performing the gait experiment include finding a way to attach the phone to the person walking. We also had trouble easily sorting the data into google sheets, but we were able to solve the problem. To minimize errors and maximize data consistency we could do what I said earlier and thoroughly study the subject matter. Also, as a class, we could all decide how far we would all walk so the amount of data we all had would be consistent. The only problem we had with google sheets, as said previously, was trying to figure how to input data but we solved this problems by talking to peers.
Challenges I encountered while working on the Whole-Class Database was what data to use and then how and what format to graph the data in. The "quality of teaming" was good and we both worked equally and solved problems together. My team collaborated and communicated with other teams when we had a question and asked if they could help us with it. We also asked clarifying questions. We didn't have problems with the data we recorded. We were able to make sufficient graphs and interpret the data. We didn't notice any shortcomings. To design a better experiment, we could do further research into the subject beforehand to make sure we all thoroughly understand the subject matter. Challenges while performing the gait experiment include finding a way to attach the phone to the person walking. We also had trouble easily sorting the data into google sheets, but we were able to solve the problem. To minimize errors and maximize data consistency we could do what I said earlier and thoroughly study the subject matter. Also, as a class, we could all decide how far we would all walk so the amount of data we all had would be consistent. The only problem we had with google sheets, as said previously, was trying to figure how to input data but we solved this problems by talking to peers.