SensorEvent

accuracy

int accuracy

The accuracy of this event. See SensorManager for details.

sensor

Sensor sensor

The sensor that generated this event. See SensorManager for details.

timestamp

long timestamp

The time in nanosecond at which the event happened

values

float[] values

The length and contents of the values array depends on which sensor type is being monitored (see also SensorEvent for a definition of the coordinate system used).

Sensor.TYPE_ACCELEROMETER:

• values[0]: Acceleration minus Gx on the x-axis
• values[1]: Acceleration minus Gy on the y-axis
• values[2]: Acceleration minus Gz on the z-axis

A sensor of this type measures the acceleration applied to the device (Ad). Conceptually, it does so by measuring forces applied to the sensor itself (Fs) using the relation:

In particular, the force of gravity is always influencing the measured acceleration:

For this reason, when the device is sitting on a table (and obviously not accelerating), the accelerometer reads a magnitude of g = 9.81 m/s^2

Similarly, when the device is in free-fall and therefore dangerously accelerating towards to ground at 9.81 m/s^2, its accelerometer reads a magnitude of 0 m/s^2.

It should be apparent that in order to measure the real acceleration of the device, the contribution of the force of gravity must be eliminated. This can be achieved by applying a high-pass filter. Conversely, a low-pass filter can be used to isolate the force of gravity.

     public void onSensorChanged(SensorEvent event)
     {
          // alpha is calculated as t / (t + dT)
          // with t, the low-pass filter's time-constant
          // and dT, the event delivery rate

          final float alpha = 0.8;

          gravity[0] = alpha * gravity[0] + (1 - alpha) * event.values[0];
          gravity[1] = alpha * gravity[1] + (1 - alpha) * event.values[1];
          gravity[2] = alpha * gravity[2] + (1 - alpha) * event.values[2];

          linear_acceleration[0] = event.values[0] - gravity[0];
          linear_acceleration[1] = event.values[1] - gravity[1];
          linear_acceleration[2] = event.values[2] - gravity[2];
     }
 

Examples:

• When the device lies flat on a table and is pushed on its left side toward the right, the x acceleration value is positive.
• When the device lies flat on a table, the acceleration value is +9.81, which correspond to the acceleration of the device (0 m/s^2) minus the force of gravity (-9.81 m/s^2).
• When the device lies flat on a table and is pushed toward the sky with an acceleration of A m/s^2, the acceleration value is equal to A+9.81 which correspond to the acceleration of the device (+A m/s^2) minus the force of gravity (-9.81 m/s^2).

Sensor.TYPE_MAGNETIC_FIELD:

Sensor.TYPE_GYROSCOPE:

• values[0]: Angular speed around the x-axis
• values[1]: Angular speed around the y-axis
• values[2]: Angular speed around the z-axis

Typically the output of the gyroscope is integrated over time to calculate a rotation describing the change of angles over the time step, for example:

     private static final float NS2S = 1.0f / 1000000000.0f;
     private final float[] deltaRotationVector = new float[4]();
     private float timestamp;

     public void onSensorChanged(SensorEvent event) {
          // This time step's delta rotation to be multiplied by the current rotation
          // after computing it from the gyro sample data.
          if (timestamp != 0) {
              final float dT = (event.timestamp - timestamp) * NS2S;
              // Axis of the rotation sample, not normalized yet.
              float axisX = event.values[0];
              float axisY = event.values[1];
              float axisZ = event.values[2];

              // Calculate the angular speed of the sample
              float omegaMagnitude = sqrt(axisX*axisX + axisY*axisY + axisZ*axisZ);

              // Normalize the rotation vector if it's big enough to get the axis
              if (omegaMagnitude > EPSILON) {
                  axisX /= omegaMagnitude;
                  axisY /= omegaMagnitude;
                  axisZ /= omegaMagnitude;
              }

              // Integrate around this axis with the angular speed by the time step
              // in order to get a delta rotation from this sample over the time step
              // We will convert this axis-angle representation of the delta rotation
              // into a quaternion before turning it into the rotation matrix.
              float thetaOverTwo = omegaMagnitude * dT / 2.0f;
              float sinThetaOverTwo = sin(thetaOverTwo);
              float cosThetaOverTwo = cos(thetaOverTwo);
              deltaRotationVector[0] = sinThetaOverTwo * axisX;
              deltaRotationVector[1] = sinThetaOverTwo * axisY;
              deltaRotationVector[2] = sinThetaOverTwo * axisZ;
              deltaRotationVector[3] = cosThetaOverTwo;
          }
          timestamp = event.timestamp;
          float[] deltaRotationMatrix = new float[9];
          SensorManager.getRotationMatrixFromVector(deltaRotationMatrix, deltaRotationVector);
          // User code should concatenate the delta rotation we computed with the current rotation
          // in order to get the updated rotation.
          // rotationCurrent = rotationCurrent * deltaRotationMatrix;
     }
 

In practice, the gyroscope noise and offset will introduce some errors which need to be compensated for. This is usually done using the information from other sensors, but is beyond the scope of this document.

Sensor.TYPE_LIGHT:

• values[0]: Ambient light level in SI lux units

Sensor.TYPE_PRESSURE:

• values[0]: Atmospheric pressure in hPa (millibar)

Sensor.TYPE_PROXIMITY:

• values[0]: Proximity sensor distance measured in centimeters

Note: Some proximity sensors only support a binary near or far measurement. In this case, the sensor should report its maximum range value in the far state and a lesser value in the near state.

Sensor.TYPE_GRAVITY:

A three dimensional vector indicating the direction and magnitude of gravity. Units are m/s^2. The coordinate system is the same as is used by the acceleration sensor.

Note: When the device is at rest, the output of the gravity sensor should be identical to that of the accelerometer.

Sensor.TYPE_LINEAR_ACCELERATION:

The output of the accelerometer, gravity and linear-acceleration sensors must obey the following relation:

acceleration = gravity + linear-acceleration

Sensor.TYPE_ROTATION_VECTOR:

The rotation vector represents the orientation of the device as a combination of an angle and an axis, in which the device has rotated through an angle θ around an axis <x, y, z>.

The three elements of the rotation vector are <x*sin(θ/2), y*sin(θ/2), z*sin(θ/2)>, such that the magnitude of the rotation vector is equal to sin(θ/2), and the direction of the rotation vector is equal to the direction of the axis of rotation.

Elements of the rotation vector are unitless. The x,y, and z axis are defined in the same way as the acceleration sensor.

• X is defined as the vector product Y.Z (It is tangential to the ground at the device's current location and roughly points East).
• Y is tangential to the ground at the device's current location and points towards magnetic north.
• Z points towards the sky and is perpendicular to the ground.

• values[0]: x*sin(θ/2)
• values[1]: y*sin(θ/2)
• values[2]: z*sin(θ/2)
• values[3]: cos(θ/2)
• values[4]: estimated heading Accuracy (in radians) (-1 if unavailable)

values[3], originally optional, will always be present from SDK Level 18 onwards. values[4] is a new value that has been added in SDK Level 18.

Sensor.TYPE_ORIENTATION:

• values[0]: Azimuth, angle between the magnetic north direction and the y-axis, around the z-axis (0 to 359). 0=North, 90=East, 180=South, 270=West

  values[1]: Pitch, rotation around x-axis (-180 to 180), with positive values when the z-axis moves toward the y-axis.

  values[2]: Roll, rotation around the y-axis (-90 to 90) increasing as the device moves clockwise.

Note: This definition is different from yaw, pitch and roll used in aviation where the X axis is along the long side of the plane (tail to nose).

Note: This sensor type exists for legacy reasons, please use rotation vector sensor type and getRotationMatrix() in conjunction with remapCoordinateSystem() and getOrientation() to compute these values instead.

Important note: For historical reasons the roll angle is positive in the clockwise direction (mathematically speaking, it should be positive in the counter-clockwise direction).

Sensor.TYPE_RELATIVE_HUMIDITY:

• values[0]: Relative ambient air humidity in percent

When relative ambient air humidity and ambient temperature are measured, the dew point and absolute humidity can be calculated.

The dew point is the temperature to which a given parcel of air must be cooled, at constant barometric pressure, for water vapor to condense into water.

                    ln(RH/100%) + m·t/(Tn+t)
 td(t,RH) = Tn · ------------------------------
                 m - [ln(RH/100%) + m·t/(Tn+t)]
 

t_d

dew point temperature in °C

t

actual temperature in °C

RH

actual relative humidity in %

m

17.62

T_n

243.12 °C

for example:

 h = Math.log(rh / 100.0) + (17.62 * t) / (243.12 + t);
 td = 243.12 * h / (17.62 - h);
 

The absolute humidity is the mass of water vapor in a particular volume of dry air. The unit is g/m³.

                    RH/100%·A·exp(m·t/(Tn+t))
 dv(t,RH) = 216.7 · -------------------------
                           273.15 + t
 

d_v

absolute humidity in g/m³

t

actual temperature in °C

RH

actual relative humidity in %

m

17.62

T_n

243.12 °C

A

6.112 hPa

for example:

 dv = 216.7 *
 (rh / 100.0 * 6.112 * Math.exp(17.62 * t / (243.12 + t)) / (273.15 + t));
 

Sensor.TYPE_AMBIENT_TEMPERATURE:

• values[0]: ambient (room) temperature in degree Celsius.

Sensor.TYPE_MAGNETIC_FIELD_UNCALIBRATED:

The values array is shown below:

• values[0] = x_uncalib
• values[1] = y_uncalib
• values[2] = z_uncalib
• values[3] = x_bias
• values[4] = y_bias
• values[5] = z_bias

x_uncalib, y_uncalib, z_uncalib are the measured magnetic field in X, Y, Z axes. Soft iron and temperature calibrations are applied. But the hard iron calibration is not applied. The values are in micro-Tesla (uT).

x_bias, y_bias, z_bias give the iron bias estimated in X, Y, Z axes. Each field is a component of the estimated hard iron calibration. The values are in micro-Tesla (uT).

Hard iron - These distortions arise due to the magnetized iron, steel or permanent magnets on the device. Soft iron - These distortions arise due to the interaction with the earth's magnetic field.

TYPE_GAME_ROTATION_VECTOR:

In the ideal case, a phone rotated and returning to the same real-world orientation will report the same game rotation vector (without using the earth's geomagnetic field). However, the orientation may drift somewhat over time. See TYPE_ROTATION_VECTOR for a detailed description of the values. This sensor will not have the estimated heading accuracy value.

Sensor.TYPE_GYROSCOPE_UNCALIBRATED:

No gyro-drift compensation is performed. Factory calibration and temperature compensation is still applied to the rate of rotation (angular speeds).

The coordinate system is the same as is used for the TYPE_ACCELEROMETER Rotation is positive in the counter-clockwise direction (right-hand rule). That is, an observer looking from some positive location on the x, y or z axis at a device positioned on the origin would report positive rotation if the device appeared to be rotating counter clockwise. The range would at least be 17.45 rad/s (ie: ~1000 deg/s).

• values[0] : angular speed (w/o drift compensation) around the X axis in rad/s
• values[1] : angular speed (w/o drift compensation) around the Y axis in rad/s
• values[2] : angular speed (w/o drift compensation) around the Z axis in rad/s
• values[3] : estimated drift around X axis in rad/s
• values[4] : estimated drift around Y axis in rad/s
• values[5] : estimated drift around Z axis in rad/s

Pro Tip: Always use the length of the values array while performing operations on it. In earlier versions, this used to be always 3 which has changed now.