I'm pretty new to signal processing. My data is the $(x,y,z)$ acceleration of someone walking with a smartphone in his/her pocket.

**Goal** 

1. Given two signals, determine if it's from the same person twice, or if it's from two separate people.
2. Have some idea of the degree of confidence in the decision from 1.

**Attempt**

1. Segmented the data into the cyclic portions (each step and retraction of foot), and acyclic portions (maybe waiting between steps or when the user stops to talk to someone).
2. Calculate the statistics of the cyclic portions, and acyclic portions (mean, s.dev, median, cross correlation, time between each step and such).

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Having said that, I lack the base knowledge for determining if two signals are 'similar' to one another which I'd imagine comes from some distance function. I've found [ResearchGate thread](https://www.researchgate.net/post/how_to_measure_the_similarity_between_two_signal) but I was wondering if there's anything else that I should include (especially since my goals are not the same as that users). The methods they mention are: 

* cross-correlation
* covariance
* mutual-information
* autocorrelation

My background is mostly stats / ML / software so I'd really appreciate it if any explanations were dumbed down!