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The systems I'm analyzing are simple enough that they can be modeled with only a few parameters, so I've actually been able to somewhat bypass the problem of numerically differentiating noisy data. As an example, when free fall position vs time data are fit by a quadratic the coefficient of the square term is a measurement of g/2. Being a global operation, linear regression is less sensitive to the small-scale noise than local differentiation is so I've gotten values pretty close to the accepted value this way.
To help with my error budget, do you have any estimates for the uncertainty in the position values generated by the path tracker?
They are slow mo videos made with the default camera app on iPhone 11.
The linear kinematics tool is neat and I especially like the built-in filter. However, the graphs don't follow the scale set by calibrated lines and all accelerations are shown as zero.
Another workaround I've found is to convert the offending .MOV file to .mp4.
Something I didn't mention in my last post is that some of the videos only play until about halfway through. Converting to .mp4 seems to get around that problem, too.
Hi Joan,
First of all, thank you for Kinovea and for all the support you give it. The problem I'm having is that some of the trajectories exported to Excel are multi-valued. That is, they have multiple rows with the same value of t. I emailed you an example. Any insight would be appreciated.
Thanks,
Dan
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