Signal processing techniques for an accelerometer signal? I am running some tests where I am recording accelerometer measurements.  I am looking to use elements of signal processing on this signal, but I am unsure about where to begin, or what my approach should be.

My ultimate goal is to be able to monitor the acceleration readings in real-time, and then display a notification when the event occurs.  As you can see around the 150,000 sample time, an event occurs.


If I am monitoring this data in real time, what sort of signal processing techniques could be implemented to react to this event?  
Would a Short-Time Fourier Transform (STFT) be an option?  


I am monitoring my data in Python, and they have a decent STFT function.  

The arguments of this function are as follows:


scipy.signal.stft(x, fs=1.0, window='hann', nperseg=256, noverlap=None, nfft=None, 
detrend=False, return_onesided=True, boundary='zeros', padded=True, axis=-1)


How do I determine optimal parameters to use to process this signal?
Are there any other methods that you folks think may help me in identifying when the event occurs in real-time (as opposed to just using the magnitude of the acceleration)?





EDIT 1:

My STFT has been added above.
I'm wondering why the STFT pops out. To me, wouldn't a simple threshold on the signal itself or on its envelope do better / just as well, after removal of the g offset?
Once you decide what &quot;measure&quot; is best to detect your event, you can apply the work of Basseville and Nikiforov, that I answered here.

The classic reference for that problem is Detection of Abrupt Changes - Theory and Application by Basseville and Nikiforov.  The whole book is available as a PDF download.
My recommendation is that you read Chapter 2.2 on the CUSUM (cumulative sum) algorithm.