/**@class android.hardware.SensorEvent @extends java.lang.Object This class represents a {@link android.hardware.Sensor Sensor} event and holds information such as the sensor's type, the time-stamp, accuracy and of course the sensor's {@link android.hardware.SensorEvent#values data}. <p> <u>Definition of the coordinate system used by the SensorEvent API.</u> </p> <p> The coordinate-system is defined relative to the screen of the phone in its default orientation. The axes are not swapped when the device's screen orientation changes. </p> <p> The X axis is horizontal and points to the right, the Y axis is vertical and points up and the Z axis points towards the outside of the front face of the screen. In this system, coordinates behind the screen have negative Z values. </p> <p> <center><img src="../../../images/axis_device.png" alt="Sensors coordinate-system diagram." border="0" /></center> </p> <p> <b>Note:</b> This coordinate system is different from the one used in the Android 2D APIs where the origin is in the top-left corner. </p> @see SensorManager @see SensorEvent @see Sensor */ var SensorEvent = { /** <p> The length and contents of the {@link #values values} array depends on which {@link android.hardware.Sensor sensor} type is being monitored (see also {@link android.hardware.SensorEvent} for a definition of the coordinate system used). </p> <h4>{@link android.hardware.Sensor#TYPE_ACCELEROMETER Sensor.TYPE_ACCELEROMETER}:</h4> All values are in SI units (m/s^2) <ul> <li> values[0]: Acceleration minus Gx on the x-axis </li> <li> values[1]: Acceleration minus Gy on the y-axis </li> <li> values[2]: Acceleration minus Gz on the z-axis </li> </ul> <p> A sensor of this type measures the acceleration applied to the device (<b>Ad</b>). Conceptually, it does so by measuring forces applied to the sensor itself (<b>Fs</b>) using the relation: </p> <b><center>Ad = - ∑Fs / mass</center></b> <p> In particular, the force of gravity is always influencing the measured acceleration: </p> <b><center>Ad = -g - ∑F / mass</center></b> <p> For this reason, when the device is sitting on a table (and obviously not accelerating), the accelerometer reads a magnitude of <b>g</b> = 9.81 m/s^2 </p> <p> 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. </p> <p> 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 <i>high-pass</i> filter. Conversely, a <i>low-pass</i> filter can be used to isolate the force of gravity. </p> <pre class="prettyprint"> 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]; } </pre> <p> <u>Examples</u>: <ul> <li>When the device lies flat on a table and is pushed on its left side toward the right, the x acceleration value is positive.</li> <li>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).</li> <li>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).</li> </ul> <h4>{@link android.hardware.Sensor#TYPE_MAGNETIC_FIELD Sensor.TYPE_MAGNETIC_FIELD}:</h4> All values are in micro-Tesla (uT) and measure the ambient magnetic field in the X, Y and Z axis. <h4>{@link android.hardware.Sensor#TYPE_GYROSCOPE Sensor.TYPE_GYROSCOPE}: </h4> All values are in radians/second and measure the rate of rotation around the device's local X, Y and Z axis. The coordinate system is the same as is used for the acceleration sensor. Rotation is positive in the counter-clockwise direction. 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. Note that this is the standard mathematical definition of positive rotation and does not agree with the definition of roll given earlier. <ul> <li> values[0]: Angular speed around the x-axis </li> <li> values[1]: Angular speed around the y-axis </li> <li> values[2]: Angular speed around the z-axis </li> </ul> <p> 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: </p> <pre class="prettyprint"> 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; } </pre> <p> 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. </p> <h4>{@link android.hardware.Sensor#TYPE_LIGHT Sensor.TYPE_LIGHT}:</h4> <ul> <li>values[0]: Ambient light level in SI lux units </li> </ul> <h4>{@link android.hardware.Sensor#TYPE_PRESSURE Sensor.TYPE_PRESSURE}:</h4> <ul> <li>values[0]: Atmospheric pressure in hPa (millibar) </li> </ul> <h4>{@link android.hardware.Sensor#TYPE_PROXIMITY Sensor.TYPE_PROXIMITY}: </h4> <ul> <li>values[0]: Proximity sensor distance measured in centimeters </li> </ul> <p> <b>Note:</b> Some proximity sensors only support a binary <i>near</i> or <i>far</i> measurement. In this case, the sensor should report its {@link android.hardware.Sensor#getMaximumRange() maximum range} value in the <i>far</i> state and a lesser value in the <i>near</i> state. </p> <h4>{@link android.hardware.Sensor#TYPE_GRAVITY Sensor.TYPE_GRAVITY}:</h4> <p>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.</p> <p><b>Note:</b> When the device is at rest, the output of the gravity sensor should be identical to that of the accelerometer.</p> <h4> {@link android.hardware.Sensor#TYPE_LINEAR_ACCELERATION Sensor.TYPE_LINEAR_ACCELERATION}: </h4> A three dimensional vector indicating acceleration along each device axis, not including gravity. All values have units of m/s^2. The coordinate system is the same as is used by the acceleration sensor. <p>The output of the accelerometer, gravity and linear-acceleration sensors must obey the following relation:</p> <p><ul>acceleration = gravity + linear-acceleration</ul></p> <h4>{@link android.hardware.Sensor#TYPE_ROTATION_VECTOR Sensor.TYPE_ROTATION_VECTOR}:</h4> <p>The rotation vector represents the orientation of the device as a combination of an <i>angle</i> and an <i>axis</i>, in which the device has rotated through an angle θ around an axis <x, y, z>.</p> <p>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.</p> </p>The three elements of the rotation vector are equal to the last three components of a <b>unit</b> quaternion <cos(θ/2), x*sin(θ/2), y*sin(θ/2), z*sin(θ/2)>.</p> <p>Elements of the rotation vector are unitless. The x,y, and z axis are defined in the same way as the acceleration sensor.</p> The reference coordinate system is defined as a direct orthonormal basis, where: </p> <ul> <li>X is defined as the vector product <b>Y.Z</b> (It is tangential to the ground at the device's current location and roughly points East).</li> <li>Y is tangential to the ground at the device's current location and points towards magnetic north.</li> <li>Z points towards the sky and is perpendicular to the ground.</li> </ul> <p> <center><img src="../../../images/axis_globe.png" alt="World coordinate-system diagram." border="0" /></center> </p> <ul> <li> values[0]: x*sin(θ/2) </li> <li> values[1]: y*sin(θ/2) </li> <li> values[2]: z*sin(θ/2) </li> <li> values[3]: cos(θ/2) </li> <li> values[4]: estimated heading Accuracy (in radians) (-1 if unavailable)</li> </ul> <p> 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. </p> <h4>{@link android.hardware.Sensor#TYPE_ORIENTATION Sensor.TYPE_ORIENTATION}:</h4> All values are angles in degrees. <ul> <li> 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 </p> <p> values[1]: Pitch, rotation around x-axis (-180 to 180), with positive values when the z-axis moves <b>toward</b> the y-axis. </p> <p> values[2]: Roll, rotation around the y-axis (-90 to 90) increasing as the device moves clockwise. </p> </ul> <p> <b>Note:</b> This definition is different from <b>yaw, pitch and roll</b> used in aviation where the X axis is along the long side of the plane (tail to nose). </p> <p> <b>Note:</b> This sensor type exists for legacy reasons, please use {@link android.hardware.Sensor#TYPE_ROTATION_VECTOR rotation vector sensor type} and {@link android.hardware.SensorManager#getRotationMatrix getRotationMatrix()} in conjunction with {@link android.hardware.SensorManager#remapCoordinateSystem remapCoordinateSystem()} and {@link android.hardware.SensorManager#getOrientation getOrientation()} to compute these values instead. </p> <p> <b>Important note:</b> For historical reasons the roll angle is positive in the clockwise direction (mathematically speaking, it should be positive in the counter-clockwise direction). </p> <h4>{@link android.hardware.Sensor#TYPE_RELATIVE_HUMIDITY Sensor.TYPE_RELATIVE_HUMIDITY}:</h4> <ul> <li> values[0]: Relative ambient air humidity in percent </li> </ul> <p> When relative ambient air humidity and ambient temperature are measured, the dew point and absolute humidity can be calculated. </p> <u>Dew Point</u> <p> 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. </p> <center><pre> ln(RH/100%) + m·t/(T<sub>n</sub>+t) t<sub>d</sub>(t,RH) = T<sub>n</sub> · ------------------------------ m - [ln(RH/100%) + m·t/(T<sub>n</sub>+t)] </pre></center> <dl> <dt>t<sub>d</sub></dt> <dd>dew point temperature in °C</dd> <dt>t</dt> <dd>actual temperature in °C</dd> <dt>RH</dt> <dd>actual relative humidity in %</dd> <dt>m</dt> <dd>17.62</dd> <dt>T<sub>n</sub></dt> <dd>243.12 °C</dd> </dl> <p>for example:</p> <pre class="prettyprint"> h = Math.log(rh / 100.0) + (17.62 * t) / (243.12 + t); td = 243.12 * h / (17.62 - h); </pre> <u>Absolute Humidity</u> <p> The absolute humidity is the mass of water vapor in a particular volume of dry air. The unit is g/m<sup>3</sup>. </p> <center><pre> RH/100%·A·exp(m·t/(T<sub>n</sub>+t)) d<sub>v</sub>(t,RH) = 216.7 · ------------------------- 273.15 + t </pre></center> <dl> <dt>d<sub>v</sub></dt> <dd>absolute humidity in g/m<sup>3</sup></dd> <dt>t</dt> <dd>actual temperature in °C</dd> <dt>RH</dt> <dd>actual relative humidity in %</dd> <dt>m</dt> <dd>17.62</dd> <dt>T<sub>n</sub></dt> <dd>243.12 °C</dd> <dt>A</dt> <dd>6.112 hPa</dd> </dl> <p>for example:</p> <pre class="prettyprint"> dv = 216.7 * (rh / 100.0 * 6.112 * Math.exp(17.62 * t / (243.12 + t)) / (273.15 + t)); </pre> <h4>{@link android.hardware.Sensor#TYPE_AMBIENT_TEMPERATURE Sensor.TYPE_AMBIENT_TEMPERATURE}: </h4> <ul> <li> values[0]: ambient (room) temperature in degree Celsius.</li> </ul> <h4>{@link android.hardware.Sensor#TYPE_MAGNETIC_FIELD_UNCALIBRATED Sensor.TYPE_MAGNETIC_FIELD_UNCALIBRATED}:</h4> Similar to {@link android.hardware.Sensor#TYPE_MAGNETIC_FIELD}, but the hard iron calibration is reported separately instead of being included in the measurement. Factory calibration and temperature compensation will still be applied to the "uncalibrated" measurement. Assumptions that the magnetic field is due to the Earth's poles is avoided. <p> The values array is shown below: <ul> <li> values[0] = x_uncalib </li> <li> values[1] = y_uncalib </li> <li> values[2] = z_uncalib </li> <li> values[3] = x_bias </li> <li> values[4] = y_bias </li> <li> values[5] = z_bias </li> </ul> </p> <p> 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). </p> <p> 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). </p> <p> 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. </p> <h4> {@link android.hardware.Sensor#TYPE_GAME_ROTATION_VECTOR Sensor.TYPE_GAME_ROTATION_VECTOR}:</h4> Identical to {@link android.hardware.Sensor#TYPE_ROTATION_VECTOR} except that it doesn't use the geomagnetic field. Therefore the Y axis doesn't point north, but instead to some other reference, that reference is allowed to drift by the same order of magnitude as the gyroscope drift around the Z axis. <p> 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 {@link android.hardware.Sensor#TYPE_ROTATION_VECTOR} for a detailed description of the values. This sensor will not have the estimated heading accuracy value. </p> <h4> {@link android.hardware.Sensor#TYPE_GYROSCOPE_UNCALIBRATED Sensor.TYPE_GYROSCOPE_UNCALIBRATED}:</h4> All values are in radians/second and measure the rate of rotation around the X, Y and Z axis. An estimation of the drift on each axis is reported as well. <p> No gyro-drift compensation is performed. Factory calibration and temperature compensation is still applied to the rate of rotation (angular speeds). </p> <p> The coordinate system is the same as is used for the {@link android.hardware.Sensor#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). <ul> <li> values[0] : angular speed (w/o drift compensation) around the X axis in rad/s </li> <li> values[1] : angular speed (w/o drift compensation) around the Y axis in rad/s </li> <li> values[2] : angular speed (w/o drift compensation) around the Z axis in rad/s </li> <li> values[3] : estimated drift around X axis in rad/s </li> <li> values[4] : estimated drift around Y axis in rad/s </li> <li> values[5] : estimated drift around Z axis in rad/s </li> </ul> </p> <p><b>Pro Tip:</b> 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. </p> <h4>{@link android.hardware.Sensor#TYPE_POSE_6DOF Sensor.TYPE_POSE_6DOF}:</h4> A TYPE_POSE_6DOF event consists of a rotation expressed as a quaternion and a translation expressed in SI units. The event also contains a delta rotation and translation that show how the device?s pose has changed since the previous sequence numbered pose. The event uses the cannonical Android Sensor axes. <ul> <li> values[0]: x*sin(θ/2) </li> <li> values[1]: y*sin(θ/2) </li> <li> values[2]: z*sin(θ/2) </li> <li> values[3]: cos(θ/2) </li> <li> values[4]: Translation along x axis from an arbitrary origin. </li> <li> values[5]: Translation along y axis from an arbitrary origin. </li> <li> values[6]: Translation along z axis from an arbitrary origin. </li> <li> values[7]: Delta quaternion rotation x*sin(θ/2) </li> <li> values[8]: Delta quaternion rotation y*sin(θ/2) </li> <li> values[9]: Delta quaternion rotation z*sin(θ/2) </li> <li> values[10]: Delta quaternion rotation cos(θ/2) </li> <li> values[11]: Delta translation along x axis. </li> <li> values[12]: Delta translation along y axis. </li> <li> values[13]: Delta translation along z axis. </li> <li> values[14]: Sequence number </li> </ul> <h4>{@link android.hardware.Sensor#TYPE_STATIONARY_DETECT Sensor.TYPE_STATIONARY_DETECT}:</h4> A TYPE_STATIONARY_DETECT event is produced if the device has been stationary for at least 5 seconds with a maximal latency of 5 additional seconds. ie: it may take up anywhere from 5 to 10 seconds afte the device has been at rest to trigger this event. The only allowed value is 1.0. <ul> <li> values[0]: 1.0 </li> </ul> <h4>{@link android.hardware.Sensor#TYPE_MOTION_DETECT Sensor.TYPE_MOTION_DETECT}:</h4> A TYPE_MOTION_DETECT event is produced if the device has been in motion for at least 5 seconds with a maximal latency of 5 additional seconds. ie: it may take up anywhere from 5 to 10 seconds afte the device has been at rest to trigger this event. The only allowed value is 1.0. <ul> <li> values[0]: 1.0 </li> </ul> <h4>{@link android.hardware.Sensor#TYPE_HEART_BEAT Sensor.TYPE_HEART_BEAT}:</h4> A sensor of this type returns an event everytime a hear beat peak is detected. Peak here ideally corresponds to the positive peak in the QRS complex of an ECG signal. <ul> <li> values[0]: confidence</li> </ul> <p> A confidence value of 0.0 indicates complete uncertainty - that a peak is as likely to be at the indicated timestamp as anywhere else. A confidence value of 1.0 indicates complete certainly - that a peak is completely unlikely to be anywhere else on the QRS complex. </p> <h4>{@link android.hardware.Sensor#TYPE_LOW_LATENCY_OFFBODY_DETECT Sensor.TYPE_LOW_LATENCY_OFFBODY_DETECT}:</h4> <p> A sensor of this type returns an event every time the device transitions from off-body to on-body and from on-body to off-body (e.g. a wearable device being removed from the wrist would trigger an event indicating an off-body transition). The event returned will contain a single value to indicate off-body state: </p> <ul> <li> values[0]: off-body state</li> </ul> <p> Valid values for off-body state: <ul> <li> 1.0 (device is on-body)</li> <li> 0.0 (device is off-body)</li> </ul> </p> <p> When a sensor of this type is activated, it must deliver the initial on-body or off-body event representing the current device state within 5 seconds of activating the sensor. </p> <p> This sensor must be able to detect and report an on-body to off-body transition within 1 second of the device being removed from the body, and must be able to detect and report an off-body to on-body transition within 5 seconds of the device being put back onto the body. </p> <h4>{@link android.hardware.Sensor#TYPE_ACCELEROMETER_UNCALIBRATED Sensor.TYPE_ACCELEROMETER_UNCALIBRATED}:</h4> All values are in SI units (m/s^2) Similar to {@link android.hardware.Sensor#TYPE_ACCELEROMETER}, Factory calibration and temperature compensation will still be applied to the "uncalibrated" measurement. <p> The values array is shown below: <ul> <li> values[0] = x_uncalib without bias compensation </li> <li> values[1] = y_uncalib without bias compensation </li> <li> values[2] = z_uncalib without bias compensation </li> <li> values[3] = estimated x_bias </li> <li> values[4] = estimated y_bias </li> <li> values[5] = estimated z_bias </li> </ul> </p> <p> x_uncalib, y_uncalib, z_uncalib are the measured acceleration in X, Y, Z axes similar to the {@link android.hardware.Sensor#TYPE_ACCELEROMETER}, without any bias correction (factory bias compensation and any temperature compensation is allowed). x_bias, y_bias, z_bias are the estimated biases. </p> @see GeomagneticField */ values : "null", /** The sensor that generated this event. See {@link android.hardware.SensorManager SensorManager} for details. */ sensor : "null", /** The accuracy of this event. See {@link android.hardware.SensorManager SensorManager} for details. */ accuracy : "null", /** The time in nanosecond at which the event happened */ timestamp : "null", };