according to sensiya(http://www.sensiya.com/) their SDK can detect motions like walking running sitting driving etc.
I guess acceleration data can be used for classifier to detect run and walk.
But sitting and driving are quite the same, what else technique they used in order to distinct driving and sitting? does anyone have any insight?
Best How To :
For full disclosure, I am working at Sensiya. Many algorithms that recognize device's user activity rely mainly on the accelerometer sensor data analytics, as you mentioned, but if you want to fine tune and expand the type of activities you want to track I suggest using other device's sensors like proximity, magnetic field etc, or just use our tools ;)
For the specific driving and still recognition technique: Differentiation of the still and driving states is a tough task. A simple solution will be to recognize that the device is in still state but its gps location changes, although this solution will not be efficient in terms of battery life. Our driving recognition tries to save battery life during this kind of recognition and we succeeded to find a slight difference between device's perfect still state and driving still state in terms of real time data you can collect from the device.
This is a good material to start with: dialnet.unirioja.es/descarga/articulo/3954593.pdf