Compute Similarity between Fourier Transforms
Most simply, you can compute simple statistical measures on each FFT frame and compare those directly. The signal energy, its distribution, variance, etc across the frequency spectrum are interesting measures. You can also directly compute the correlation coefficient between accelerometer signal pairs (normalized to their length).

It might be useful to look into research on “gait recognition”, the literature points to “low frequency-domain entropy” as feature that is useful in discriminating accel/gyro data that differ in “complexity”

Have you seen this thread on comparing 3d data from accelerometers. It is worth considering if using the raw 3d data, doing a dimensionality reduction (PCA, etc) and then using a simple distance metric may get you further than analyzing raw FFT output. Doing an FFT as you've proposed doing is only one way to simplify the data, but I think the Fourier transform is too coarse &amp; noisy to give you directly comparable results, every time.

As an alternative, it might be easier/faster to use machine learning, i.e. training a "people walking" classifier using any modern supervised learning technique.