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Before we get back to Android programming, we need some theoretical background on signal analysis.The document is somewhat heavy on math. To quote Dr. Kaufman's Fortran Coloring Book: if you don't like it, skip it. But if your teacher likes it, you failed.
Click here to read the report.
If you are too impatient to read, here is the essence.
There is no single perfect algorithm when analysing acceleration signals. The analysis framework should provide a toolbox of different algorithms, some working in the time-domain, some operating in the frequency domain. The decision engine that classifies the movements may use a number of algorithms, a characteristic set for each movement type.
It has been concluded in the medical research community that wavelet transformation is the most optimal algorithm for frequency-domain analysis of acceleration signals. This report presented concrete cases, how wavelet transformation can be used to classify three common movements: walking, running and shake. In addition, the wavelet transformation provided data series that can be used to extract other interesting information, e.g. step count.
For those who would like to repeat my experiments, I uploaded the prototype. First
you need Sage (I used version 4.3.3). Download and unpack the prototype package and enter the proto directory. Then launch Sage (with the "sage" command) and issue the following commands:
import accel
accel.movements(5)
now you will be able to look at the different waveforms, e.g.
list_plot(accel.shake_w5)
Sage is scriptable in Python. If you know Python, you will find everything familiar, if not - bad luck, you won't get far in Sage.
Click here to read the report.
If you are too impatient to read, here is the essence.
There is no single perfect algorithm when analysing acceleration signals. The analysis framework should provide a toolbox of different algorithms, some working in the time-domain, some operating in the frequency domain. The decision engine that classifies the movements may use a number of algorithms, a characteristic set for each movement type.
It has been concluded in the medical research community that wavelet transformation is the most optimal algorithm for frequency-domain analysis of acceleration signals. This report presented concrete cases, how wavelet transformation can be used to classify three common movements: walking, running and shake. In addition, the wavelet transformation provided data series that can be used to extract other interesting information, e.g. step count.
For those who would like to repeat my experiments, I uploaded the prototype. First
you need Sage (I used version 4.3.3). Download and unpack the prototype package and enter the proto directory. Then launch Sage (with the "sage" command) and issue the following commands:
import accel
accel.movements(5)
now you will be able to look at the different waveforms, e.g.
list_plot(accel.shake_w5)
Sage is scriptable in Python. If you know Python, you will find everything familiar, if not - bad luck, you won't get far in Sage.
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