Modification of Accelerometer Data for Feature Extraction I have accelerometer [x,y,z] data recorded from various activities.  I am not looking at any filtering techniques (which I have seen within this forum) because I want to be able to measure the amplitude of the noise for each activity sample set using a global time-domain feature average loudness.

I am curious to know if I can center the signal around 0g, similarly to the X Data Minus Mean vector plot, but without the x-axis orientation i.e. the signal shifting up and down?

I have the x-axis data from one of my activities below:



I was hoping that by subtracting the mean from the x-axis data would yield this, but all it has done is shifted the entire signal down to around 0g.

The reason I ask is because I am unsure if computing a time-domain feature such as average loudness will work with the signal shifting up and down?

Average Loudness/Energy:
Your data is composed of a majoriy of low-activity regions, with higher-amplitiude  sparse/concentrated activities. The bad news is that most of the methods to achieve your goal are likely to somehow filter your data. The good news is that non-linear filtering could help you. 

If you are not concerned with calculations and real-time yet, I would suggest first a centered median filter to extract a robust $0 g$ estimate, and remove it from the data. In other words: choose an appropriate left-right span of $K$. In each window around index $k$, computes the signal $\hat{s}(k) = s(k) - \textrm{median} [s_{k-K},\ldots,s_{k+K}]$. 

A median is a non-linear filter minimizing the $ell_1$ norm (like the $ell_2$ norm minimization yields the standard mean). It is somewhat robust to outliers, and since yours are both above and below your thought $0 g$ estimate, a running median (computed around each sample) could extract this 0-level reference.

Looking at your plots, start with $K=50,100,200$ to see if this starts to provides you with what you are looking for. Then, more involded techniques could be considered.