Reconstructing Digital Signals Using Shannon's Sampling Theorem

in Journal of Applied Biomechanics
Restricted access

Purchase article

USD $24.95

Student 1 year subscription

USD $87.00

1 year subscription

USD $116.00

Student 2 year subscription

USD $165.00

2 year subscription

USD $215.00

Researchers must be cognizant of the frequency content of analog signals that they are collecting. Knowing the frequency content allows the researcher to determine the minimum sampling frequency of the data (Nyquist critical frequency), ensuring that the digital data will have all of the frequency characteristics of the original signal. The Nyquist critical frequency is 2 times greater than the highest frequency in the signal. When sampled at a rate above the Nyquist, the digital data will contain all of the frequency characteristics of the original signal but may not present a correct time-series representation of the signal. In this paper, an algorithm known as Shannon's Sampling Theorem is presented that correctly reconstructs the time-series profile of any signal sampled above the Nyquist critical frequency. This method is superior to polynomial or spline interpolation techniques in that it can reconstruct peak values found in the original signal but missing from the sampled data time-series.

The authors are with the Biomechanics Laboratory, Department of Exercise Science, University of Massachusetts, Amherst, MA 01003.

Journal of Applied Biomechanics
Article Metrics
All Time Past Year Past 30 Days
Abstract Views 56 56 24
Full Text Views 6 6 0
PDF Downloads 11 11 0
Altmetric Badge
PubMed
Google Scholar