Detect oscillations

Question

I'm trying to use an Arduino and a MMA8452Q accelerometer to sense movement and detect oscillations. What would be the best way to analyze and process the info to detect oscillations at a specific frequency range and act accordingly?

Edit 1:

Aswering Michael Kellett questions

Must you detect a single frequency (known in advance) or a band of frequencies.

I must detect if the oscillation is in the 4-8Hz range.

Is the sampling rate under direct contorl of the Arduino or is it set by the accelerometer.

It is mostly under control of the Arduino. The accelerometer is set to sample at a certain rate but I can "miss steps" simply by reading the I2C interface at a much lower rate.

What is the range of frequencies and amplitudes you must detect.

I must detect if the oscillation is in the 4-8Hz range. The amplitude will be handled by the accelerometer but I expect it to be between ±4G's

Are there other signals present at the same time which you must ignore.

No, but it would be nice to be able to log the results.

How quickly must you reach a decision when the signal appears.

I haven't decided that yet but the oscillation must persist for several seconds before action is taken. The exact number of seconds hasn't still been determined.

What accelerometer are you suing.

MMA8452Q I2C accelerometer

D_Hersey · Accepted Answer

I think Fourier (actually probably developed by Leonhard Euler) would be real useful here because the signal data is so sinuate. FFT is commonly used to refer to spectral transforms generally, as I meant. But you are technically correct, strictly speaking 'FFT' refers a specific algorithmic trick. This would be a very small problem if we had access to the analog signal, we could be on our way with a dual op-amp and some Rs and Cs. The Fourier Transform is (like, say, negation) self-inverse: Therefore it does not lose (aside from quantization) information. Binoculars turn images into spectral data and back in order to have the optical system track. Works fine. Further I never suggested real-time FFT, just that it would be a useful tool for looking at the data. Voluntary movements are readily distinguishable from spasms.

michaelkellett · Answer

Hello Enrique,

It makes it easier for people to follow the thread if you just add a new message rather than editing the original question.

It seems that you want to detect energy in the band 4 - 8Hz, not too big an issue with external noise, using an MMA8452Q I2C accelerometer.

The accelerometer can be set to sample at 50 or 100Hz and the data must be bandpass filtered to accept frequencies between 4 and 8 Hz. The simplest (good) way to measure the amplitude of the incoming signal is to compute the square root of the continuous average of the square of the input samples.

If you know how to do the filtering and averaging then you can start coding - if not then come back here and I'll try to give some pointers. (Do you have access to the student edition of MATLAB ?)

MK

Detect oscillations

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