How do I control sensor data readings against (measured) outside influences?
In short, I think the focus of your question is mostly on how to deal with covariates (watch out for the not always clear use of this term and its synonyms - see here and here. What I mean by covariates is &quot;In statistics, a covariate represents a source of variation that has not been controlled in the experiment and is believed to affect the dependent variable.&quot;)
If we first take a step back and ask what you really want from your analysis, it is to my understanding to investigate the relationship between two (or more) variables. These would be e.g. EDA values on one hand and some output variable such as e.g. emotional states based on heart beat data on the other (since I don't have much information on your outcome variable(s) at this point, I will simply call them y). Thus, you have EDA values as an independent variable and y as a dependent variable. So far, so good - bringing together dependent and independent variables statistically can be done in many ways, ranging from even just calculating a correlation between the two (given they are both numeric variables) across fitting linear models and up to machine learning techniques etc. Let us for now go with setting up a linear model:
Short-hand notation: y = EDA
Same model written out more detailed: y = intercept + EDA + error
You already know, however, that you have this other outside influence that affects your EDA-values. Luckily, you even tried to measure/quantify this influence via the accelerometer. Thus, besides dependent variables that you are interested in and the independent variables you are interested in, you now have a third type of variable: the independent variables you are not really interested in, but that you expect to have an influence on the variables that you are interested in. This is what I mean by covariates. Taking a covariate into a model can greatly improve the statistical model in terms of its ability to actually analyze the variables we are interested in. In other words: In the optimal case, a covariate explains so much of the unexplained noise in the data, that the variability that is left lets us draw conclusions about the relationship between the other variables that we could not see before. So the model from above may become:
y = EDA + COVARIATE
y = intercept + EDA + COVARIATE + error
Thus, the question comes up on how to decide whether or not to include such a covariate in your model/analysis. One analysis that is very much in this realm is ANCOVA - analysis of covariance.
I will stop here to make sure I understood the problem correctly and would be glad to hear whether this is helping so far.