Motion Detector and Video Analysis Lab #1

 1. Measurement of acceleration due to gravity from a Motion Detector

We used LoggerPro to measure the acceleration due to gravity of a basketball given from the position vs time and velocity vs time graphs.

Procedure:

• Set up the motion detector by mounting it around 4-5 feet above the ground on the table.
• Then connected the detector to LoggerPro on the laptop and set up the position vs. time and velocity vs. time graph.
• Dropped the basketball from right under the motion detector, and the computer recorded the movement as the ball hit the floor with the total time being around 2 seconds. 
• Applied the curve fit for the position vs time graph and linear fit for the velocity vs. time graph.

Results:

Standard Deviation: Data came from us, Yvette, Enbo, Elle, and Steven, respectively

2. Measurement of acceleration due to gravity from Video Analysis

I used the video analysis app to measure the acceleration due to gravity of a basketball as it bounces through the frame of the video. I got the acceleration values by looking at the position vs. time and velocity vs. time graphs.

Procedure:

• Look for an area where I could record the video.
• Once I found a place, I set up the meter stick on the floor and launched the basketball starting at the start of the meter stick and recorded until the ball bounced out of frame. 
• Analyzed the video by uploading it into the video analysis app and found the graphs for the horizontal and vertical motion.
• Applied the curve fit for the position vs. time graph and linear fit for the velocity vs. time graph to find the acceleration.

Results:

Horizontal Position:

Horizontal Velocity:

Vertical Position:

Vertical Velocity:

Acceleration Calculations:

Standard Deviation: Data came from me, Derek, Ryan, Michael, and Dilay, respectively

3. Write-up on measurement variability

For the motion detector and video analysis experiments, my measurements do not agree within the uncertainty from the standard deviations. For the motion detector, the measurements that have uncertainty are the height of the detector, the starting point of the ball relative to the detector, how inflated the ball was, and human error/how the ball was dropped. For the video analysis, the measurements that have uncertainty are how accurate we were in marking the pixels of the ball as it moves through the frame, the alignment of the ball and the meter stick on the same plane, the resolution/frame rate of the camera. For the video analysis specifically, my value was much higher whcih means some human errors may have occured as well. When it comes to the estimated uncertainty from LoggerPro and Video Analysis (below), the measurements also do not agree within the uncertainty. Though both experiments are useful, the motion detector measurements are more useful because standard deviation is less than the video analysis measurements. There are also less factors involved in producing uncertainties with the motion detector. For example, for the video analysis, factors such as the camera, meter stick placement, etc. can play a factor in producing mixed results. 

Percent Difference, Estimated Uncertainty, and Uncertainty Propagation