Noise rejection / feature extraction
I've tried a copule of methods on your data (low-pass filtering via DFT, SSA filtering), but I don't know what result will satisfy you.

You mentioned that the signal is sampled with a higher-then-enough frequency. Can you rise the  sampling frequency even higher? I'm talking about oversampling. This can help to get rid of some part (some kind) of the noise present.

You also mentioned PCA and that it's not appropriate for univariate time series. Right, PCA is almost useles with univariate time series, but there is an appropriate and closely related technique, SSA. You can find detailed description here: Singular Spectrum Analysis for Time Series (free sample is enough: Chapter 2: Basic SSA). And my own GNU Octave (hope Matlab compatible) implementation here: ssa-octave.m (there is also Scilab implementation, but you probably don't need it). Take a look at chapter 2.4 "Choice of Parameters in Basic SSA" for interpretation and tuning tips.

It's also not clear to me: the "5 seconds of stationary" pattern in isometric data sample should be filtred out or it is a useful signal too?