2 Collecting Data

In this lab you will compare measurements of acceleration from the accelerometer and wheel sensors to determine which one is the most accurate.

Lab 2 Worksheet Collecting Data

Learning Goals:

1. Collect data from multiple sources.
2. Compare data using mean, standard deviation, and standard error of the mean.

Part 1: Collecting Deceleration Data

1. Calibrate the accelerometer to your computer.
2. From the Sensor list, choose the Accelerometer sensor. You will only need to look at the y-axis so you can uncheck “A_x” and “A_z“.
3. Also from the Sensor list choose the Wheel sensor. You will only need to look at the Accelerometer vs time graph that the Wheel sensor displays so you can uncheck the “Position” and “Velocity” boxes.
4. Place the iOLab device wheel side down on a flat surface. Then start Recording and apply a quick push to the Force sensor. The force should be strong enough that the iOLab can roll for a couple of seconds afterward.
5. The iOLab software will save your data automatically so you can look at it later but you should label it so it is easy to find later. Above the Accelerometer graph you will see a folder icon. Click on this and the first “Acquisition” will be the data you just took. Click on the pencil icon and then write something in the “Comment” box. Write something that will make sense when you go back to look at the data later. Then be sure to click “Save”.  See below.

6. Zoom in so you can see the data in more detail. You can see below my Accelerometer data before and after zooming. Do the same for your Acceleration vs time graph.

7. Determine the part of the graphs that represent the time when the iOLab device was slowing down. Use the data tool to highlight the part of the graph that represents the device slowing down. Record the times at the beginning and at the end of the highlighted section. This will show as “t” above the graph on the left.
8. Record the average acceleration (μ) and standard deviation (σ) during this time from the Accelerometer graph and the Acceleration vs time graph. (You can see that the acceleration is negative so we could call this negative acceleration or deceleration.)
9. Below the graph you will see an icon of a square with an arrow. Click on this and then click on “Export to CSV”.

10. This will save your csv file to a folder in your Documents folder called “IOLab-WorkFiles”. Within this folder is another folder caled “export”. Here is where you will find your data. Open this data with a spreadsheet. You will use this to determine how many measurements the accelerometer sensor took during this time. The A column in this spreadsheet counts the number of measurements taken and the D column shows the time. Use the times you recorded in step 7 to determine how many measurements were taken. Call this “N”.
11. In order to compare the data you collect in this lab, you will need to write it out in the following way: mean ± standard error of the mean. You already recorded the mean, μ, from the accelerometer graph. Standard error of the mean can be calculated by dividing the standard deviation, σ, by the square root of the number of measurements, N. Record your accelerometer data in the form of mean ± standard error of the mean.
12. Interestingly, the wheel sensor and accelerometer sensor do not collect the same number of measurements. You will need to export the spreadsheet for the wheel sensor and find the number of measurements it took. Then record your wheel sensor acceleration data in the form of mean ± standard error of the mean.
13. Compare the accelerometer and wheel sensor data. Which one do you think is more accurate? Why?

Part 2: Collecting Data at Rest and Falling

1. Click “Reset” a couple times and this time only display A_z on the Accelerometer sensor. Place the device on a flat surface wheel side up and begin recording. Record the average acceleration along the z-axis. What did you think it would read? Why?
2. Now lift the iOLab device a couple feet in the air. Again, orient it so that the wheels are facing up. Then, with someone prepared to catch it, start recording data and drop the iOLab device. Record the average acceleration from the Accelerometer sensor while it is falling? What did you think it would read? Why?

Accelerometer sensors are tricky to understand. They don’t always do what we would expect them to do. When you pushed the device horizontally, the data you collected indicated that the sensor was measuring the rate at which the iOLab device was slowing down. But when you dropped it, it appeared to measure something different. The difference was that gravity was involved in the vertical direction and not in the horizontal direction. Whenever you are using an accelerometer sensor to determine actual changes in velocity, you have to consider whether or not gravity is affecting the measurement. If any part of your measurement involves the z-axis, or some portion of it, gravity has to be factored in. The calculation to factor gravity out is very complicated but if you want to measure acceleration with gravity factored out there are phone apps that do this.

Part 3: Noise

When you zoomed in on your graphs, you should have noticed that the line was not completely flat. Even in Part 2 where you just had the iOLab device sitting flat on the table and not moving, the line was not flat. If you didn’t notice that before, go zoom in now to see.

What you are seeing is called “Noise” or “Random Uncertainty”. This type of uncertainty is caused by unknown or unpredictable changes. They usually have a normal distribution. Export the data from the accelerometer when the iOLab device was at rest. You will need the data from Column J called “cal[2]”. Make a copy of this spreadsheet: Normal Distribution Spreadsheet. Paste your accelerometer sensor data into Column B of this spreadsheet. You should get a nice bell curve shape.

Grading Rubric:

Points are assigned for each worksheet question according to the following:

Lab 2 Grading Rubric 131 Manual