I'm implementing an accelerometer-based pedometer, and I was wondering if there was any known algorithms to handle that.
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There's an interesting paper (with source code) here that may be of help: http://www.analog.com/static/imported-files/application_notes/47076299220991AN_900.pdf.
The charts are interesting. If I were to do this myself I would probably sample the data at a fairly high frequency, convert to frequency domain with a FFT, apply a digital band-pass filter to cut off all frequencies outside the expected minimum/maximum walking speeds (including any DC offset), do a reverse-FFT to reconstruct the now-filtered signal and then run the resulting data through an edge-detector with a Hysteresis function. This is all pure speculation of course but looking at those charts I think it would work, it would be relatively fast to code up and well within the processing power of a mobile phone.
You have probably found this:
Enhancing the Performance of Pedometers Using a Single Accelerometer
Anyhow, I am also interested in finding a good algorithm, I am curios what other answers you will get. :)
There is an app called Sensor data that you can uses to gather experimental data so you can then analyze it and try to find an algorithm.
Its going to be quite tricky to find a very good algorithm especially for the iPhone since its accelerometer is quite noisy