Displacement from Acceleration data

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SUMMARY

The discussion focuses on extracting displacement from acceleration data using the Fast Fourier Transform (FFT) method. The process involves four key steps: removing the mean from the acceleration data, applying the FFT, converting the transformed data to displacement by dividing by -omega², and finally performing an inverse FFT to return to the time domain. This method is highlighted as an effective alternative to traditional double integration techniques, which may not yield satisfactory results.

PREREQUISITES
  • Understanding of acceleration data and its characteristics
  • Familiarity with Fast Fourier Transform (FFT) concepts
  • Knowledge of inverse FFT operations
  • Basic grasp of frequency domain analysis
NEXT STEPS
  • Study the mathematical principles behind Fast Fourier Transform (FFT)
  • Learn about the process of removing mean values from datasets
  • Explore the conversion of frequency domain data to time domain using inverse FFT
  • Investigate the application of FFT in signal processing and displacement measurement
USEFUL FOR

Engineers, data analysts, and researchers involved in motion analysis, signal processing, or those specifically working with accelerometer data for displacement calculations.

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I was looking for some help on getting displacement from acceleration data (using an accelerometer). I found this old PF thread - https://www.physicsforums.com/archive/index.php/t-123128.html
I am familiar with the double integration/trapezoidal integration. It isn't much help.
The FFT method seems interesting. I don't understand it though.
Is there an explanation of this method. Does it have a name.

Please see FFT method mention by wolfpackdiver in the link above -
1. Remove the mean from the accel. data
2. Take the Fourier transform (FFT) of the accel. data.
3. Convert the transformed accel. data to displacement data by dividing each element by -omega^2, where omega is the frequency band.
4. Now take the inverse FFT to get back to the time-domain and scale your result.
 
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