Creating features from raw accelerometer data
This is an active area of research called Human Activity Recognition. There are several public datasets available to cross-validate your methods, and you might want to start here: UCI HAR Dataset. There's a paper that accompanies the dataset that describes their preprocessing methods, so you'll want to have a look at that and see if anything helps in terms of feature engineering.
There are plenty of regression-based models that exist in the exercise science space, but I've had good results using 1D convolutional neural networks on very simply transformed data. We just downsampled the signal (by averaging) to 1 or 2 Hz and reduced the 3 axes to a single time series via euclidean vector magnitude (our subjects were performing higher-intensity activities, which often cause the sensor to rotate on their limb, changing the direction of each axis' input - reducing to EVM removes that potential confounding).
I'm not totally sure what your data looks like - do you have transitions (from sitting to standing and back)? I wouldn't use PCA (there is some argument that LDA can work if you're careful). There's something called tICA (time-series independent components analysis) but I don't think that's what you want because it's reducing the dimensionality at each time step (and each of your time steps is only 3 features). You're probably going to be going with fourier features (see the link above) or simple downsample / EVM.
Another concern here is that it's possible that the data isn't reliably separable because the intensity of each activity is low. How long is each &quot;event&quot; (I'm understanding each event to be a &quot;bout&quot; of activity with some duration)? My models have, in the past, had difficulty separating certain low-intensity activity bouts because if one person isn't moving standing up and another person isn't moving sitting down, the model can't tell 0 from 0 and is basically guessing.  Hopefully there's enough signal in there to get a good result, it just depends on your experimental setup.