I have to find the best approach for tackling a problem for trying to recognize physical movements - with an iPhone in a pocket - like waling, stopping, turning left/right, sitting.
I was thinking on just heuristically find the data corresponding to each action, then to check the incoming values against this data (with a threshold) and see what's happening. That's a very rough approach, of course, so maybe using something like the Support Vector Machine methods, but this seems too complicated for the amount of time I have to develop this.
Which approach would you suggest here?
Walking: Do an fft on the gravity direction signal. Measure its frequency response for walking at different speeds and then set a simple threshold.
Stopping: if the average power i.e. total energy in the signal over the last few seconds drops below a certain threshold then you can say the user has stopped.
Turning left,right: Use the gravity vector and the gyroscopes rotation speed vector to determine whether the user is rotating clockwise or counterclockwise
Sitting: This will be very hard to determine but if youre lucky the SVM will find the right pattern.
Each of the above can be given a weighting and then you will have to find a good way to obtain training data to train your SVM. Maybe stream the signals from the phone to a webserver and simultaneously record the users motions by hand.
Your best starting point is Apples Sample code: CoreMotionTeapot
Alternatively you could analyze the GPS signal. This will give you a very good way to determine the users larger scale motion like walking/moving or changing heading etc.
来源：https://stackoverflow.com/questions/5489115/accelerometer-movement-pattern-recognition-iphone