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A fixed sampling window can be hard to manage, especially if you want the calculation to be recursive, for space and speed considerations.

You can achieve similar results via exponential smoothing.

Y_n = (1−α) Y_n−1 + α X_n

Where Y_n is the smoothed outcome, X_n is the current measurement, and α is the weight.

With some thoughts, it can be done with integers and without loss of precision.

edit: i ran a comparison of exponential smoothing vs. (fixed window) moving average. The moving average algorithm was written to be recursive for maximal speed.

the sampled data is a simulated sine wave:

1000 * sin(2pi*n/100) + 100 * noise. n = 0..19999, and noise = -0.5...+0.5.

So snr = 1000 / 50 = 20:1, or about 5 digits. far worse than what you would encounter in a 10-bit adc. enter image description here

the window I picked is 4.

As you can see, both approaches closely resemble each other. exponential smoothing uses much less ram and is also much faster. older data has lower weight on the output, something generally desirable in most applications.