gravity is an array of 3 floats containing the gravity vector expressed in the device's coordinate. You can simply use the values returned by a SensorEvent of a Sensor of type TYPE_ACCELEROMETER. This is noted in SensorManager.getRotationMatrix, but I want to know why not use values of TYPE_GRAVITY ? I think it will be better than using TYPE_ACCELEROMETER.because ACCELEROMETER = GRAVIDY - LINEAR_ACCELEROMETER, ACCELEROMETER won't point to the earth when moving.
The three sensors Sensor.TYPE_ACCELEROMETER, Sensor.TYPE_GRAVITY and Sensor.TYPE_LINEAR_ACCELERATION are related by the equation
"Sensor.TYPE_ACCELEROMETER" = "Sensor.TYPE_GRAVITY" + "Sensor.TYPE_LINEAR_ACCELERATION"
If you register for updates for all three, what you find is that Sensor.TYPE_ACCELEROMETER always arrives first, followed by Sensor.TYPE_GRAVITY and Sensor.TYPE_LINEAR_ACCELERATION, and that the values always satisfy that equation. Internally, Android is using filtering, and in particular a Kalman filter, to separate the two.
A low pass filter is a simple way of doing something similar. However, a lot of thought will have gone into the Android mechanism, so I'm sure that if a low pass filter was better, then Android would have implemented that.
I have an app where I poll the sensors for acceleration data and save the XYZ values to a SQL DB. Codewise it is pretty simple:
public void onSensorChanged(SensorEvent event) {
sensor = event.sensor;
int i = sensor.getType();
if (i == MainActivity.TYPE_ACCELEROMETER) {
accelerometerMatrix = event.values;
} else if (i == MainActivity.TYPE_GYROSCOPE) {
gyroscopeMatrix = event.values;
} else if (i == MainActivity.TYPE_GRAVITY) {
gravityMatrix = event.values;
} else if (i == MainActivity.TYPE_MAGNETIC) {
magneticMatrix = event.values;
}
//insert into database
}
MainActivity.TYPE_ACCELEROMETER is just a field I set in my MainActivity class.
If I set this to Sensor.TYPE_LINEAR_ACCELERATION I notice that the values that get stored to my DB are fairly low in accuracy (only 2 decimal places). But if I use the hardware accelerometer Sensor.TYPE_ACCELERATION then the accuracy is much higher (6dp)
My question is, am I retrieving the linear acceleration values incorrectly to cause me to only get 2dp of accuracy? Or is this just a limitation of how the sensors are fused together in Android? If its the latter, how do I get higher accuracy in my accelerometer values, after filtering out gravity, when working with fused sensors?
As far as I know, you cannot change accuracy of the hardware sensors on Android. You can only adjust sampling period with registerListener() method from SensorManager class or monitor accuracy change with onAccuracyChanged() callback from SensorEventListener interface.
It looks that you are retrieving sensor readings correctly in your code snippet.
I would suggest doing the following things:
try to perform the same measurement with just one sensor and
check if application behavior has changed - maybe multiple readings
are causing measurement inaccuracy?
try to run this application on a different device if you have one and verify if behavior and accuracy is different - maybe accuracy of the sensors is different on different devices?
check accuracy and datatypes in your database and verify if it handles more accurate data correctly
If you don't notice any difference after applying mentioned suggestions, I think we cannot do anything about increasing sensor readings accuracy.
Moreover, I performed some experiments with sensors on Android some time ago using Nexus 5 and Nexus 6 and I see that different sensors has different accuracy as you also noticed. According to my knowledge, we cannot control that from the Android SDK API level.
There are many examples here in stackoverflow on how to combine the Accelerometer and Gyroscope in order to retrieve the Azimuth, Pitch and Roll.
Is it possible to calculate the Yaw/Heading of the device, by using the rotation matrix from the SensorManager.getOrientation()?
SensorManager.getOrientation(float[] R, float[] values) requires a rotation matrix as input. The output is stored in the values array.
values[0] is the azimuth/yaw/heading (all the same thing).
Since there is no rotation matrix from SensorManager.getOrientation(), the answer to your original question is no.
However, SensorManager.getOrientation() can be used to extract the azimuth/yaw/heading (as well as pitch and roll) from a rotation matrix that you got from somewhere else.
There is no magical way of getting the output. You either have to use one of the built in fused sensors (like the rotation vector), or implement your own solution with the base sensors (gyroscope, accelerometer, magnetometer). Be aware that you will depend on the magnetometer to determine your azimuth/yaw/heading, since none of the other sensors can measure that aspect of orientation.
Sensor Manager documentation
I am making a compass for android and I have it working but it has one flaw. The reading on the compass is consistently changing by a couple of degrees even when the device is not moving. I know that this is due to the magnetic distortion messing with sensor but how can I make my app still responsive while the user moves yet not have so much variation displayed when the user is still. So how can I give the appearance of a more steady compass?
You have to smooth the signal. How about displaying the average of the last N values instead of displaying the raw last value ?
Have you tried implementing a low pass filter? http://blog.thomnichols.org/2011/08/smoothing-sensor-data-with-a-low-pass-filter
If you want to write a compass, you'll need to low-pass filter both acceleration and magnetic field.
Here's an example of a low-pass filter. Not very elegant, but just to show the principle:
float filteredValue;
void lowPassFilter(float rawValue) {
final float alpha = 0.8f;
float filteredValue = alpha * filteredValue + (1.0f - alpha) * rawValue;
return filteredValue;
}
Note that filteredValue is remembered after each call (like a member variable).
On the other hand, rawValue is the value from the sensor event, like event.values[0].
Notice how every 'fresh' new rawValue only affects 20% of the filtered value (that's given by alpha). This brings 'stability' to the reading, at the cost of a delay.
I wish to get the acceleration vector of an Android phone. The problem is, the accelerometer coordinates are relative to the phone's rotation. What I want is the "absolute" acceleration, i.e., it should return the same values whichever way the phone is facing. (I want to detect if a user that is skiing is sliding down a slope without using GPS. I also need to be able to differentiate sliding and going up the chairlift.)
I can probably get those values by combining the accelerometer with the gyroscope, but I have no idea how I could offset the accelerometer's values with the gyroscope's.
Is this possible, and if so, how?
What you describe can't be done, unless you redefine the problem a bit. To help you redefine it, I'll outline the main issues:
First, I'm guessing that what you mean by "absolute acceleration" is acceleration with respect to geographical reference. The can't be done with the accelerometer alone, since it has no idea about geographical references. If you move far enough for the gps, or use the compass, you might be able to get around this, but each of these has its own issues (though at least the problem is soluble).
The second issue is that gravity and acceleration are completely indistinguishable using an accelerometer alone (this is known as the "equivalence principle"). Therefore, any measured acceleration will always be the vector sum of gravity and the acceleration, but there are always multiple solutions to these equations, and in the usual cases where the acceleration is smaller than gravity, you really can't determine anything about the acceleration. Since gravity is somewhat constant though, there are ways around this too, using, say, a gyroscope, or maybe your user could hold the phone in a fixed orientation (e.g., by looking at external cues like the horizon), and either of these approaches might let you subtract the influence of gravity, but it's generally a non-trivial problem.
The final point to not is that you seem to be thinking in an earth-fixed coordinate system and the phone's accelerometer is only phone-fixed. That is the accelerometer's z-axis many not have anything to do with up and down on the earth -- and the relationship will depend on the orientation of the phone. Really, many people would prefer an earth-fixed system, but the phone just doesn't know that. You can use external cues (GPS, magnetic field, gyroscope, gravity, horizon, etc) to try to align them, but given only a single arbitrary reading form the accelerometer, the information just isn't there.
Definitions:
acceleration vector: this is the x, y, z reading from the accelerometer (and each reading will depend on the phones orientation), sometimes written as A=(ax, ay, az).
acceleration magnitude: this is a=sqrt(ax2 + ay2 + az2), and this should not depend on the phones orientation (if the different axes are calibrated to be the same). If the phone is stationary, this will basically just be a reading of gravity. Note also that a lot of the information in the acceleration vector is lost using this measure.
normalized acceleration: The acceleration direction, that has magniture 1, i.e., A/a
acceleration in earth coordinates: I think this is what you really want, there's just no easy way to get it, and really even if you could, I don't think it would be as useful as it might seem at first.
Skiing:
I think you have a good shot at determining when someone is skiing based on the measurements from the accelerometer. Things like bumps and turns should all be quite distinctive using the accelerometer. For these I'd use the full acceleration vector. For example, in turns, the acceleration magnitude would stay roughly constant and the direction would sweep. Also note that free-fall (i.e., basically whenever the skier doesn't have their skies/feet/butt/etc on the ground, whether they're going upward when launching off a bump/jump, or falling out of the chairlift), the acceleration magnitude will be zero in free-fall. For the chairlift, it seems that it will likely have a distinctive rhythmic sway mostly within a single plane.
All of these things could be figured out. I'd recommend, if you really want to solve this problem, is to record data from your accelerometer while skiing, and see if you can determine when you're skiing based on the characteristics of the data. (My guess is, that your major stumbling block with this will be math, because it might be a bit tricky to come up with an algorithm the can distinguish the signatures of skiing, so it seems that it would be a good idea to review vector math, and things like dot-products and cross-products, and also, I suspect that a little bit on another topic known as FFTs or Fourier transforms might be useful in sorting out the time and frequency signatures of skiing vs swinging in the chair lift.)
You could also fold in GPS measurements, which wouldn't be as reliable, or give good time resolution, but could at least be used to double-check your algorithm.
You can calculate acceleration regardless of the phone's orientation using:
a = sqrt(x*x + y*y + z*z)
Where a is the absolute acceleration and x, y and z are accelerometer values for each of the phone's 3 axes.
Some phones have a barometer (air pressure sensor) built in. After applying a moving average, I have found it to be write ready to determine if the user is going up or down -perhaps useful for your problem. On the galaxy s4 and 5 I get a resolution good enough to determine whether the device just moved from the table, to the floor.
Note, gradual changes in weather will affect your readings, so you must consider the Delta over a reasonable time interval, and ignore changes around some threshold.
Consider using the GPS. In a flight logging app I use the acceleration (albeit the absolute value, not the vector) to filter noisy GPS data (I remove locations where the acceleration needed for the change in speed is not plausible):
/**
* Remove noise from the measurement of the location.
* #param loc a location
* #return Answer <code>false</code> iff the location should not be used.
*/
private boolean filterNoise(final Location loc) {
if( ! loc.hasSpeed() )
return true;
if( this.recentSpeeds.isEmpty() ) { // rescentSpeeds is a queue of locations
this.recentSpeeds.add(loc);
return true;
}
final Location lastFix = this.recentSpeeds.getHead();
final long delta_t = (loc.getTime() - lastFix.getTime()) / 1000;
if( delta_t == 0 )
return false;
final float delta_v = loc.getSpeed() - lastFix.getSpeed();
final float a = delta_v / delta_t;
if( Math.abs(a) <= AccelThreshold ) {
this.recentSpeeds.add(loc);
return true;
}
return false;
}
If you compute the speed using the coordinates from the last fix and the current fix you get the acceleration as vector.