Develop Reference

Android accelerometer calibration?

TL;DR
How come the accelerometer values I get from Sensor.TYPE_ACCELEROMETER are slightly offset? I don't mean by gravity, but by some small error that varies from axis to axis and phone to phone.
Can I calibrate the accelerometer? Or is there a standard way of compensating for these errors?
I'm developing an app that has a need for as precise acceleration measurements as possible (mainly vertical acceleration, i.e. same direction as gravity).
I've been doing A LOT of testing, and it turns out that the raw values I get from Sensor.TYPE_ACCELEROMETER are off. If I let the phone rest at a perfectly horizontal surface with the screen up, the accelerometer shows a Z-value of 9.0, where it should be about 9.81. Likewise, if I put the phone in portrait or landscape mode, the X- and Y- accelerometer values show about 9.6. instead of 9.81.
This of course affects my vertical acceleration, as I'm using SensorManager.getRotationMatrixFromVector(), to calculate the vertical acceleration, resulting in a vertical acceleration that is off by a different amount depending on the rotation of the device.
Now, before anyone jumps the gun and mentions that I should try using Sensor.TYPE_LINEAR_ACCELERATION instead, I must point out that I'm actually doing that as well, parallel to the TYPE_ACCELERATION. By using the gravity sensor I then calculate the vertical acceleration (as described in this answer). The funny thing is that I get EXACTLY the same result as the method that uses the raw accelerometer, SensorManager.getRotationMatrixFromVector() and matrix multiplication (and finally subtracting gravity).
The only way I'm able to get almost exactly zero vertical acceleration for a stationary phone in any rotation is to get the raw accelerometer values, add an offset (from earlier observations, i.e. X+0.21, Y+0.21 and Z+0.81) and then performing the rotation matrix stuff to get the world coordinate system accelerations. Note that since it's not just the calculated vertical acceleration that is wrong - it's actually the raw values from Sensor.TYPE_ACCELEROMETER, which I would think excludes other error sources like gyroscope sensor, etc?
I have tested this on two different phones (Samsung Galaxy S5 and Sony Xperia Z3 compact), and both have these accelerometer value deviances - but of course not the same values on both phones.
How come the the values of Sensor.TYPE_ACCELEROMETER are off, and is there a better way of "calibrating" the accelerometer than simply observing how much they deviate from gravity and adding the difference to the values before using them?

You should calibrate gains, offsets, and angle of the 3 accelerometers.
Unfortunately it's not possible to deepen the whole topic here.
I'll write a small introduction, describing the basic concept, and then I'll post a link to the code of a simple Clinometer that implements the calibration.
The calibration routine could be done with 7 misurations (calculate the mean value of a good number of samples) in different ortogonal positions at your choice, in order to have all +-0 and +-g values of your accelerometers. For example:
STEP 1 = Lay flat
STEP 2 = Rotate 180°
STEP 3 = Lay on the left side
STEP 4 = Rotate 180°
STEP 5 = Lay vertical
STEP 6 = Rotate 180° upside-down
STEP 7 = Lay face down
Then you can use the 7 measurements mean[][] to calculate offsets and gains:
calibrationOffset[0] = (mean[0][2] + mean[0][3]) / 2;
calibrationOffset[1] = (mean[1][4] + mean[1][5]) / 2;
calibrationOffset[2] = (mean[2][0] + mean[2][6]) / 2;
calibrationGain[0] = (mean[0][2] - mean[0][3]) / (STANDARD_GRAVITY * 2);
calibrationGain[1] = (mean[1][4] - mean[1][5]) / (STANDARD_GRAVITY * 2);
calibrationGain[2] = (mean[2][0] - mean[2][6]) / (STANDARD_GRAVITY * 2);
using the values of mean[axis][step], where STANDARD_GRAVITY = 9.81.
Then apply the Gain and Offset Corrections to measurements:
for (int i = 0; i < 7; i++) {
mean[0][i] = (mean[0][i] - calibrationOffset[0]) / calibrationGain[0];
mean[1][i] = (mean[1][i] - calibrationOffset[1]) / calibrationGain[1];
mean[2][i] = (mean[2][i] - calibrationOffset[2]) / calibrationGain[2];
}
and finally calculates the correction angles:
for (int i = 0; i < 7; i++) {
angle[0][i] = (float) (Math.toDegrees(Math.asin(mean[0][i]
/ Math.sqrt(mean[0][i] * mean[0][i] + mean[1][i] * mean[1][i] + mean[2][i] * mean[2][i]))));
angle[1][i] = (float) (Math.toDegrees(Math.asin(mean[1][i]
/ Math.sqrt(mean[0][i] * mean[0][i] + mean[1][i] * mean[1][i] + mean[2][i] * mean[2][i]))));
angle[2][i] = (float) (Math.toDegrees(Math.asin(mean[2][i]
/ Math.sqrt(mean[0][i] * mean[0][i] + mean[1][i] * mean[1][i] + mean[2][i] * mean[2][i]))));
}
calibrationAngle[2] = (angle[0][0] + angle[0][1])/2; // angle 0 = X axis
calibrationAngle[1] = -(angle[1][0] + angle[1][1])/2; // angle 1 = Y axis
calibrationAngle[0] = -(angle[1][3] - angle[1][2])/2; // angle 2 = Z axis
You can find a simple but complete implementation of a 3-axis calibration in this opensource Clinometer app: https://github.com/BasicAirData/Clinometer.
There is also the APK and the link of the Google Play Store if you want to try it.
You can find the calibration routine in CalibrationActivity.java;
The calibration parameters are applied in ClinometerActivity.java.
Furthermore, you can find a very good technical article that deepens the 3-axis calibration here: https://www.digikey.it/it/articles/using-an-accelerometer-for-inclination-sensing.

Detect if device has taken a turn using location service android

I want to detect if the user has taken a turn on the road while driving using the sensors on the android phone. How do I code this? I am collecting data live from all the sensors(accelerometer,location,rotation,geomagnetic) and storing them on the sd card. So now i just want to know whether the user has a taken a turn and in which direction he has turned.

I assume the registration of the sensor is done properly. You can detect the direction by using the orientation sensor (deprecated) as follows:
#Override
public void onSensorChanged(SensorEvent event) {
float azimuth_angle = event.values[0];
int precision = 2;
if (prevAzimuth - azimuth_angle < precision * -1)
Log.v("->", "RIGHT");
else if (prevAzimuth - azimuth_angle > precision)
Log.v("<-", "LEFT");
prevAzimuth = azimuth_angle;
}
Note: The variable of "prevAzimuth" is declared as global. You can change "precision" value to whatever you want. We need this value because we do not want to see output after each trivial change in azimuth angle. However, too large precision gives imprecise results. To me, "2" is optimum.

If you are tracking location coordinates, you can also track shifts between the angle from previous locations.
angle = arctan((Y2 - Y1) / (X2 - X1)) * 180 / PI
See this answer for calculating x and y.
Decision to use sensor values is based on an unrealistic assumption that the device is never rotated with respect to the vehicle.

Approximate indoor positioning using the integration of the linear acceleration

I am trying to calculate the approximate position of an Android phone in a room. I tried with different methods such as location (wich is terrible in indoors) and gyroscope+compass. I only need to know the approximate position after walking during 5-10seconds so I think the integration of linear acceleration could be enough. I know the error is terrible because of the propagation of the error but maybe it will work in my setup. I only need the approximate position to point a camera to the Android phone.
I coded the double integration but I am doing sth wrong. IF the phone is static on a table the position (x,y,z) always keep increasing. What is the problem?
static final float NS2S = 1.0f / 1000000000.0f;
float[] last_values = null;
float[] velocity = null;
float[] position = null;
float[] acceleration = null;
long last_timestamp = 0;
SensorManager mSensorManager;
Sensor mAccelerometer;
public void onSensorChanged(SensorEvent event) {
if (event.sensor.getType() != Sensor.TYPE_LINEAR_ACCELERATION)
return;
if(last_values != null){
float dt = (event.timestamp - last_timestamp) * NS2S;
acceleration[0]=(float) event.values[0] - (float) 0.0188;
acceleration[1]=(float) event.values[1] - (float) 0.00217;
acceleration[2]=(float) event.values[2] + (float) 0.01857;
for(int index = 0; index < 3;++index){
velocity[index] += (acceleration[index] + last_values[index])/2 * dt;
position[index] += velocity[index] * dt;
}
}
else{
last_values = new float[3];
acceleration = new float[3];
velocity = new float[3];
position = new float[3];
velocity[0] = velocity[1] = velocity[2] = 0f;
position[0] = position[1] = position[2] = 0f;
}
System.arraycopy(acceleration, 0, last_values, 0, 3);
last_timestamp = event.timestamp;
}
These are the positions I get when the phone is on the table (no motion). The (x,y,z) values are increasing but the phone is still.
And these are the positions after calculate the moving average for each axis and substract from each measurement. The phone is also still.
How to improve the code or another method to get the approximate position inside a room?

There are unavoidable measurement errors in the accelerometer. These are caused by tiny vibrations in the table, imperfections in the manufacturing, etc. etc. Accumulating these errors over time results in a Random Walk. This is why positioning systems can only use accelerometers as a positioning aid through some filter. They still require some form of dead reckoning such as GPS (which doesn't work well in doors).
There is a great deal of current research for indoor positioning systems. Some areas of research into systems that can take advantage of existing infrastructure are WiFi and LED lighting positioning. There is no obvious solution yet, but I'm sure we'll need a dedicated solution for accurate, reliable indoor positioning.
You said the position always keeps increasing. Do you mean the x, y, and z components only ever become positive, even after resetting several times? Or do you mean the position keeps drifting from zero?
If you output the raw acceleration measurements when the phone is still you should see the measurement errors. Put a bunch of these measurements in an Excel spreadsheet. Calculate the mean and the standard deviation. The mean should be zero for all axes. If not there is a bias that you can remove in your code with a simple averaging filter (calculate a running average and subtract that from each result). The standard deviation will show you how far you can expect to drift in each axis after N time steps as standard_deviation * sqrt(N). This should help you mathematically determine the expected accuracy as a function of time (or N time steps).

Brian is right, there are already deployed indoor positioning systems that work with infrastructure that you can easily find in (almost) any room.
One of the solutions that has proven to be most reliable is WiFi fingerprinting. I recommend you take a look at indoo.rs - www.indoo.rs - they are pioneers in the industry and have a pretty developed system already.

This may not be the most elegant or reliable solution, but in my case it serves the purpose.
Note In my case, I am grabbing a location before the user can even enter the activity that needs indoor positioning.. and I am only concerned with a rough estimate of how much they have moved around.
I have a sensor manager that is creating a rotation matrix based on the device orientation. (using Sensor.TYPE_ROTATION_VECTOR) That obviously doesn't give me movement forward, backward, or side to side, but instead only the device orientation. With that device orientation i have a good idea of the user's bearing in degrees (which way they are facing) and using the Sensor_Step_Detector available in KitKat 4.4, I make the assumption that a step is 1 meter in the direction the user is facing..
Again, I know this is not full proof or very accurate, but depending on your purpose this too might be a simple solution..
everytime a step is detected i basically call this function:
public void computeNewLocationByStep() {
Location newLocal = new Location("");
double vAngle = getBearingInDegrees(); // returns my users bearing
double vDistance = 1 / g.kEarthRadiusInMeters; //kEarthRadiusInMeters = 6353000;
vAngle = Math.toRadians(vAngle);
double vLat1 = Math.toRadians(_location.getLatitude());
double vLng1 = Math.toRadians(_location.getLongitude());
double vNewLat = Math.asin(Math.sin(vLat1) * Math.cos(vDistance) +
Math.cos(vLat1) * Math.sin(vDistance) * Math.cos(vAngle));
double vNewLng = vLng1 + Math.atan2(Math.sin(vAngle) * Math.sin(vDistance) * Math.cos(vLat1),
Math.cos(vDistance) - Math.sin(vLat1) * Math.sin(vNewLat));
newLocal.setLatitude(Math.toDegrees(vNewLat));
newLocal.setLongitude(Math.toDegrees(vNewLng));
stepCount =0;
_location = newLocal;
}

Android - How to approach fall detection algorithm

I want to be able to feature a fairly simple fall detection algorithm in my application. At the moment in onSensorChanged(), I am getting the absolute value of the current x,x,z values and subtracting SensorManager.GRAVITY_EARTH (9.8 m/s) from this. The resulting value has to be bigger than a threshold value 10 times in a row to set a flag saying a fall has been detected by the accelerometer, the threshold value is about 8m/s.
Also I'm comparing the orientation of the phone as soon as the threshold has been passed and the orienation of it when the threshold is no longer being passed, this sets another flag saying the orientation sensor has detected a fall.
When both flags are set, an event occurs to check is user ok, etc etc. My problem is with the threshold, when the phone is held straight up the absolute value of accelerometer is about 9.8 m/s, but when i hold it still at an angle it can be over 15m/s. This is causing other events to trigger the fall detection, and if i increase the threshold to avoid that, it won't detect falls.
Can anyone give me some advice here with what possible values i should use or how to even improve my method? Many thanks.

First, I want to remind you that you cannot just add the x, y, z values together as they are, you have to use vector mathematics. This is why you get values of over 15 m/s. As long as the phone is not moving, the vector sum should always be about 9.8 m/s. You calculate it using SQRT(x*x + y*y + z*z). If you need more information, you can read about vector mathematics, maybe http://en.wikipedia.org/wiki/Euclidean_vector#Length is a good start for it.
I also suggest another algorithm: In free fall, all three of the x,y,z values of the accelerometer should be near zero. (At least, that's what I learned in physics classes a long time ago in school.) So maybe you can use a formula like if the vector sum of x,y,z <= 3 m/s than you detect a free fall. And if the vector sum then raises to a value over 20 m/s, than you detect the landing.
Those thresholds are just a wild guess. Maybe you just record the x,y,z values in a test application, and then move around the phone, and then analyze offline how the values (and their normal and vector sum) behave to get a feeling for which thresholds are sensible.

I have acutally published a paper on this issue. Please feel free to check out "ifall" # ww2.cs.fsu.edu/~sposaro
We basically take the root sum of squares and look for 3 things
1. Lower threshold broke. Ie fallinging
2. Upper threshold broke. Ie hitting the ground
3. Flatline around 1g, ie longlie, laying on the ground for an extended period of time

I forgot to update this thread, but iFall is now available on the Android Market.
Also check out ww2.cs.fsu.edu/~sposaro/iFall for more information

Its possible using the Accelerometer sensor.
Write this in the sensor changed listener..
if (sensor == Sensor.TYPE_ACCELEROMETER) {
long curTime = System.currentTimeMillis();
// only allow one update every 100ms.
if ((curTime - lastUpdate) > 100) {
long diffTime = (curTime - lastUpdate);
lastUpdate = curTime;
x = values[SensorManager.DATA_X];
y = values[SensorManager.DATA_Y];
z = values[SensorManager.DATA_Z];
float speed = Math.abs(x + y + z - last_x - last_y - last_z) / diffTime * 10000;
Log.d("getShakeDetection", "speed: " + speed);
if (speed > DashplexManager.getInstance().SHAKE_THRESHOLD) {
result = true;
}
last_x = x;
last_y = y;
last_z = z;
}
}

Android orientation as steering - roll corrupting pitch?

I am working on a project which includes an Android application which is used for
controlling/steering.
Speed: When you tilt the phone forward/backwards (pitch) it simulates giving gas and breaking.
Direction: When you tilt the phone left/right (roll) it simulates steering to the left and right.
I have already written some code which seemed to work fine. But when I took a closer look, I found that some values are acting weird.
When I tilt the phone forward/backward to handle the speed it works perfect I get the expected speed and direction values. But when I tilt the phone to the left/right to handle the direction it seems to corrupt some values. When it is tilting to the left/right that doesn't only change the direction value (roll) but it also affects the speed value (pitch).
For extra information:
Programming for Android 2.2
Device is an Google Nexus One
Holding the device in portrait
The most relevant code I use to read the sensor values is as follows:
public void onSensorChanged(SensorEvent sensorEvent)
{
synchronized (this)
{
if (sensorEvent.sensor.getType() == Sensor.TYPE_ORIENTATION)
{
float azimuth = sensorEvent.values[0]; // azimuth rotation around the z-axis
float pitch = sensorEvent.values[1]; // pitch rotation around the x-axis
float roll = sensorEvent.values[2]; // roll rotation around the y-axis
System.out.println("pitch: " + pitch);
System.out.println("roll: " + roll);
System.out.println("--------------------");
// Convert the sensor values to the actual speed and direction values
float speed = (pitch * 2.222f) + 200;
float direction = roll * -2.222f;
So when I run the code, and I look at the printed values. When tilting the device left/right, it seems to affect the pitch value as well. How come? And how can I get the pure pitch value, when 'roll'-ing? So that tilting the phone to the left/right doesn't affect/corrupt the pitch value.

You could read up on Gimbal lock. That's bitten me before.