The averaging balancing act once more comes into play. By using an ISR that accumulates data we've reduced response time, and by using the convolution we've reduced smearing. Well, as the saying goes, life is hard and then you die. Then, compute the worst case smearing the system can tolerate. Pick coefficients that will give this much smearing or less.
Brute force is ugly, but sometimes one has no choice.
I usually model the coefficients in a spreadsheet. Feed sample data in and plot the results to see just how bad the smearing effect will be. It's a quick way to check a formal analysis. With all of the discussion in magazines now about neural networks I wonder if we couldn't use a learning algorithm to adjust these coefficients. We've looked at smearing and response time.