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where L denotes the length of sliding data frame.
The whole length of sliding window that is extended for 10 μs to both sides from central point is totally 21 μs.
The length of sliding was 2 mm, the speed was 10 mm/s, and the load was 4.9 N for 40-min tests.
where RMS (t) is the RMS EMG signal at time t; y(i) is the i th sample of the raw EMG signal; N is the length of sliding window which contains samples of 200 ms before time t.
Supposing that the length of sliding window in calculating the IFR value at a certain time instant is N w, it can be approved that [20] CRBleft{IFRleft {t}_iright)right}kern0.5em =kern0.5em frac{1}{pi^2}frac{90{f}_s^4}{N_w^5SNR}.
Rectangular window function is defined as follows: (2) w (n ) = { 1, 0 ≤ n ≤ L w − 1 0, otherwise, here L w is the length of sliding window.
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The sliding window is w= [k,k+L−1], where k and L are the starting range cell and the length of the sliding window, respectively.
The length of the sliding window is 2R m −1.
Then, the length of the sliding velocity vector V f → is obtained.
The step length of the sliding window is around 4 pixels.
Therefore, the length of the sliding window can be estimated accurately when the M2M4 algorithm works well.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com