Acceleration detection accounting for rotation

AI Thread Summary
The discussion focuses on developing an app to detect vehicle acceleration and braking using a smartphone's accelerometer and gyroscope. The user has successfully isolated gravitational effects and is using a formula to calculate linear acceleration but struggles to differentiate between acceleration and braking due to changing phone orientation. It is suggested that incorporating gyroscope data can help track orientation changes, but the user seeks clarification on the necessary math and physics for this implementation. Additionally, combining GPS data is recommended to correct for accumulated errors over time and improve accuracy. The conversation emphasizes the importance of understanding how to relate accelerations across different orientations to determine the direction of acceleration accurately.
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Hello everyone!

I am trying to write an app that needs to be able to tell if a vehicle is accelerating or breaking. In this sense I used the accelerometer in the phone. At the moment I managed to isolate and subtract the gravitational pull of the Earth from my readings and I am using a noise variable to ignore minor changes (smaller than 10^-2).

In order to compute the acceleration relative to the ground I am using the following formula sqrt(Lx^2+Ly^2+Lz^2), where Lx represents the linear acceleration along the x-Axis in m/s^2.

My problem is: How do I differentiate between braking and accelerating, as my final acceleration value will always be greater than 0 and the phone can change it's rotation in between measurements (i.e. X and Y axes changing places or anything inbetween). Also I need this to work even if the vehicle accelerates -> maintains speed -> brakes/accelerates some more.

I am guessing this can be solved by using the gyroscope in the phone, which reads the rotation along the 3 axes in rad/s. However I do not know the math and physics behind it and therefor find myself unable to implement it. Is my reasoning wrong? Have I made some false assumptions? Would another way be better?
 
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If you have access to rotation data, you can track the orientation of the phone in space just based on that (relative to some starting orientation). If you also know the initial velocity, you can keep track of the velocity (as 3D vector).
This method will be very imprecise if you want to use it longer than a few seconds, as you have no way to correct the noise so errors build up over time. To get accurate speed estimates, you probably want to use the GPS data as well, ideally combined with position detection based on nearby cell phone towers.
 
I will be using these readings as a plausibility test for the gps data in order to account for possible inaccuracies, as well as in tunnels and other scenarios where the gps is unavailable. Also I will be using the gps data in order to eliminate the errors that build up over time. Could you please explain the math and physics behind tracking the orientation relative to the starting orientation? How should I account for the orientation changes, given the rotation along the 3 axes in rad/s? Ideally I would like to be able to compare 2 sets of values, with different orientations and be able to tell if the acceleration is positive or negative (braking) along the 3 axes.
I know that braking is just acceleration in the opposite direction, but don't know how to tell the direction once the orientation has changed.
 
There are many ways to store orientation. I guess the most convenient one would be to store the orientation of one phone axis (let's call it z') in the surrounding space (with x,y,z), together with the rotation angle in the x'/y' plane. Those are easy to adjust based on rotation sensors, and it is not too hard to relate the x', y', z' accelerations to accelerations in x,y,z.
 
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