src/devices/sensors/sensor-types.jd

page.title=Sensor types
@jd:body

<!--
    Copyright 2014 The Android Open Source Project

    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at

        http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
-->
<div id="qv-wrapper">
  <div id="qv">
    <h2>In this document</h2>
    <ol id="auto-toc">
    </ol>
  </div>
</div>

<p>This section describes sensor axes, base sensors, and composite sensors
(activity, attitude, uncalibrated, and interaction).</p>

<h2 id="sensor_axis_definition">Sensor axes</h2>
<p>Sensor event values from many sensors are expressed in a specific frame that
is static relative to the device.

<h3 id=phone_axes>Mobile device axes</h3>
<p>The Sensor API is relative only to the natural orientation of the screen
(axes are not swapped when the device's screen orientation changes.</p>

<img src="http://developer.android.com/images/axis_device.png" alt="Coordinate
system of sensor API for mobile devices"/>
<p class="img-caption"><strong>Figure 1.</strong> Coordinate system (relative to
a mobile device) used by the Sensor API.</p>

<h3 id=auto_axes>Automotive axes</h3>
<p>In Android Automotive implementations, axes are defined with respect to the
vehicle body frame:</p>

<img src="images/axis_auto.png" alt="Coordinate system of sensor API for
automotive devices"/>
<p class="img-caption"><strong>Figure 2.</strong> Coordinate system (relative to
an automotive device) used by the Sensor API.</p>

<ul>
<li>X increases towards the right of the vehicle</li>
<li>Y increases towards the nose of the body frame</li>
<li>Z increases towards the roof of the body frame</li>
</ul>

<p>When looking from the positive direction of an axis, positive rotations are
counterclockwise. Thus, when a vehicle is making a left turn, the z-axis
gyroscope rate of turn is expected to be a positive value.</p>

<h2 id="base_sensors">Base sensors</h2>
<p>Base sensor types are named after the physical sensors they represent. These
sensors relay data from a single physical sensor (as opposed to composite
sensors that generate data out of other sensors). Examples of base sensor types
include:</p>
<ul>
  <li><code>SENSOR_TYPE_ACCELEROMETER</code></li>
  <li><code>SENSOR_TYPE_GYROSCOPE</code></li>
  <li><code>SENSOR_TYPE_MAGNETOMETER</code></li>
</ul>

<p class='note'><strong>Note:</strong> For details on each Android sensor type,
review the following sections.</p>

<p>However, base sensors are not equal to and should not be confused with their
underlying physical sensor. The data from a base sensor is <strong>not</strong>
the raw output of the physical sensor because corrections (such as bias
compensation and temperature compensation) are applied.</p>

<p>For example, the characteristics of a base sensor might be different from the
characteristics of its underlying physical sensor in the following use cases:</p>
<ul>
<li>A gyroscope chip rated to have a bias range of 1 deg/sec.
  <ul>
    <li>After factory calibration, temperature compensation and bias compensation are
      applied, the actual bias of the Android sensor will be reduced, may be to a
      point where the bias is guaranteed to be below 0.01deg/sec.</li>
    <li>In this situation, we say that the Android sensor has a bias below 0.01
      deg/sec, even though the data sheet of the underlying sensor said 1 deg/sec.</li>
  </ul>
</li>
<li>A barometer with a power consumption of 100uW.
  <ul>
    <li>Because the generated data needs to be transported from the chip to the SoC,
      the actual power cost to gather data from the barometer Android sensor might be
      much higher, for example 1000uW.</li>
    <li>In this situation, we say that the Android sensor has a power consumption of
      1000uW, even though the power consumption measured at the barometer chip leads
      is 100uW.</li>
  </ul>
</li>
<li>A magnetometer that consumes 100uW when calibrated, but consumes more when
calibrating.
  <ul>
    <li>Its calibration routine might require activating the gyroscope, consuming
      5000uW, and running some algorithm, costing another 900uW.</li>
    <li> In this situation, we say that the maximum power consumption of the
      (magnetometer) Android sensor is 6000uW.</li>
    <li>In this case, the average power consumption is the more useful measure, and it
      is what is reported in the sensor static characteristics through the HAL.</li>
  </ul>
</li>
</ul>
<h3 id="accelerometer">Accelerometer</h3>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_ACCELEROMETER)</code> <em>returns a non-wake-up sensor</em></p>
<p>An accelerometer sensor reports the acceleration of the device along the 3
  sensor axes. The measured acceleration includes both the physical acceleration
  (change of velocity) and the gravity. The measurement is reported in the x, y
  and z fields of sensors_event_t.acceleration.</p>
<p>All values are in SI units (m/s^2) and measure the acceleration of the device
  minus the force of gravity along the 3 sensor axes.</p>
<p>Here are examples:</p>
<ul>
  <li> The norm of (x, y, z) should be close to 0 when in free fall. </li>
  <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 along z is +9.81
    alo, which corresponds 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, the
    acceleration value is greater than +9.81, which corresponds to the acceleration
    of the device (+A m/s^2) minus the force of gravity (-9.81 m/s^2). </li>
</ul>
<p>The readings are calibrated using:</p>
<ul>
  <li> temperature compensation </li>
  <li> online bias calibration </li>
  <li> online scale calibration </li>
</ul>
<p>The bias and scale calibration must only be updated while the sensor is
  deactivated, so as to avoid causing jumps in values during streaming.</p>
<p>The accelerometer also reports how accurate it expects its readings to be
  through <code>sensors_event_t.acceleration.status</code>. See the <a
  href="http://developer.android.com/reference/android/hardware/SensorManager.html">SensorManager</a>’s
  <a href="http://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH"><code>SENSOR_STATUS_*  </code></a> constants for more information on possible values for this field.</p>
<h3 id="ambient_temperature">Ambient temperature</h3>
<p>Reporting-mode: <em><a href="report-modes.html#on-change">On-change</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_AMBIENT_TEMPERATURE)</code> <em>returns a non-wake-up sensor</em></p>
<p>This sensor provides the ambient (room) temperature in degrees Celsius.</p>
<h3 id="magnetic_field_sensor">Magnetic field sensor</h3>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_MAGNETIC_FIELD)</code> <em>returns a non-wake-up sensor</em></p>
<p><code>SENSOR_TYPE_GEOMAGNETIC_FIELD == SENSOR_TYPE_MAGNETIC_FIELD</code></p>
<p>A magnetic field sensor (also known as magnetometer) reports the ambient
  magnetic field, as measured along the 3 sensor axes.</p>
<p>The measurement is reported in the x, y and z fields of
  <code>sensors_event_t.magnetic</code> and all values are in micro-Tesla (uT).</p>
<p>The magnetometer also reports how accurate it expects its readings to be
  through <code>sensors_event_t.magnetic.status</code>. See the <a
href="http://developer.android.com/reference/android/hardware/SensorManager.html">SensorManager</a>’s
<a href="http://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH"><code>SENSOR_STATUS_*</code></a> constants for more information on possible values for this field.</p>
<p>The readings are calibrated using:</p>
<ul>
  <li> temperature compensation </li>
  <li> factory (or online) soft-iron calibration </li>
  <li> online hard-iron calibration </li>
</ul>
<h3 id="gyroscope">Gyroscope</h3>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_GYROSCOPE)</code> <em>returns a non-wake-up sensor</em></p>
<p>A gyroscope sensor reports the rate of rotation of the device around the 3
  sensor axes.</p>
<p>Rotation is positive in the counterclockwise 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. Note that this is the standard
  mathematical definition of positive rotation and does not agree with the
  aerospace definition of roll.</p>
<p>The measurement is reported in the x, y and z fields of <code>sensors_event_t.gyro</code>
  and all values are in radians per second (rad/s).</p>
<p>The readings are calibrated using:</p>
<ul>
  <li> temperature compensation </li>
  <li> factory (or online) scale compensation </li>
  <li> online bias calibration (to remove drift) </li>
</ul>
<p>The gyroscope also reports how accurate it expects its readings to be through
  <code>sensors_event_t.gyro.status</code>. See the <a
  href="http://developer.android.com/reference/android/hardware/SensorManager.html">SensorManager</a>’s
  <a
href="http://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH"><code>SENSOR_STATUS_*</code></a> constants for more information on possible values for this field.</p>
<p>The gyroscope cannot be emulated based on magnetometers and accelerometers, as
  this would cause it to have reduced local consistency and responsiveness. It
  must be based on a usual gyroscope chip.</p>
<h3 id="heart_rate">Heart Rate</h3>
<p>Reporting-mode: <em><a href="report-modes.html#on-change">On-change</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_HEART_RATE)</code> <em>returns a non-wake-up sensor</em></p>
<p>A heart rate sensor reports the current heart rate of the person touching the
  device.</p>
<p>The current heart rate in beats per minute (BPM) is reported in
  <code>sensors_event_t.heart_rate.bpm</code> and the status of the sensor is reported in
  <code>sensors_event_t.heart_rate.status</code>. See the <a
  href="http://developer.android.com/reference/android/hardware/SensorManager.html">SensorManager</a>’s
  <a href="http://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH"><code>SENSOR_STATUS_*</code></a> constants for more information on possible values for this field. In
  particular, upon the first activation, unless the device is known to not be on
  the body, the status field of the first event must be set to
  <code>SENSOR_STATUS_UNRELIABLE</code>. Because this sensor is on-change,
  events are generated when and only when <code>heart_rate.bpm</code> or
  <code>heart_rate.status</code> have changed since the last event. The events
  are generated no faster than every <code>sampling_period</code>.</p>
<p><code>sensor_t.requiredPermission</code> is always <code>SENSOR_PERMISSION_BODY_SENSORS</code>.</p>
<h3 id="light">Light</h3>
<p>Reporting-mode: <em><a href="report-modes.html#on-change">On-change</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_LIGHT)</code> <em>returns a non-wake-up sensor</em></p>
<p>A light sensor reports the current illumination in SI lux units.</p>
<p>The measurement is reported in <code>sensors_event_t.light</code>.</p>
<h3 id="proximity">Proximity</h3>
<p>Reporting-mode: <em><a href="report-modes.html#on-change">On-change</a></em></p>
<p>Usually defined as a wake-up sensor</p>
<p><code>getDefaultSensor(SENSOR_TYPE_PROXIMITY)</code> <em>returns a wake-up sensor</em></p>
<p>A proximity sensor reports the distance from the sensor to the closest visible
  surface.</p>
<p>Up to Android KitKat, the proximity sensors were always wake-up sensors, waking
  up the SoC when detecting a change in proximity. After Android KitKat, we
  advise to implement the wake-up version of this sensor first, as it is the one
  that is used to turn the screen on and off while making phone calls.</p>
<p>The measurement is reported in centimeters in <code>sensors_event_t.distance</code>. Note
  that some proximity sensors only support a binary &quot;near&quot; or &quot;far&quot; measurement.
  In this case, the sensor report its <code>sensor_t.maxRange</code> value in the &quot;far&quot; state
  and a value less than <code>sensor_t.maxRange</code> in the &quot;near&quot; state.</p>
<h3 id="pressure">Pressure</h3>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_PRESSURE)</code> <em>returns a non-wake-up sensor</em></p>
<p>A pressure sensor (also known as barometer) reports the atmospheric pressure in
  hectopascal (hPa).</p>
<p>The readings are calibrated using</p>
<ul>
  <li> temperature compensation </li>
  <li> factory bias calibration </li>
  <li> factory scale calibration </li>
</ul>
<p>The barometer is often used to estimate elevation changes. To estimate absolute
  elevation, the sea-level pressure (changing depending on the weather) must be
  used as a reference.</p>
<h3 id="relative_humidity">Relative humidity</h3>
<p>Reporting-mode: <em><a href="report-modes.html#on-change">On-change</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_RELATIVE_HUMIDITY)</code> <em>returns a non-wake-up sensor</em></p>
<p>A relative humidity sensor measures relative ambient air humidity and returns a
  value in percent.</p>

<h2 id="composite_sensor_types">Composite sensor types</h2>
<p>A composite sensor generates data by processing and/or fusing data from one
or several physical sensors. (Any sensor that is not a base sensor is called a
composite sensor.) Examples of composite sensors include:</p>
<ul>
<li><a href="#step_detector">Step detector</a> and
<a href="#significant_motion">Significant motion</a>, which are usually based on
an accelerometer, but could be based on other sensors as well, if the power
consumption and accuracy was acceptable.</li>
<li><a href="#game_rotation_vector">Game rotation vector</a>, based on an
accelerometer and a gyroscope.</li>
<li><a href="#gyroscope_uncalibrated">Uncalibrated gyroscope</a>, which is
similar to the gyroscope base sensor, but with the bias calibration being
reported separately instead of being corrected in the measurement.</li>
</ul>
<p>As with base sensors, the characteristics of the composite sensors come from
the characteristics of their final data. For example, the power consumption of a
game rotation vector is probably equal to the sum of the power consumptions of
the accelerometer chip, the gyroscope chip, the chip processing the data, and
the buses transporting the data. As another example, the drift of a game
rotation vector depends as much on the quality of the calibration algorithm as
on the physical sensor characteristics.</p>

<p>The following table lists available composite sensor types. Each composite
sensor relies on data from one or several physical sensors. Avoid choosing other
underlying physical sensors to approximate results as they provide a poor user
experience.</p>
<p class="note"><strong>Note:</strong> When there is no gyroscope on the device
(and only when there is no gyroscope), you may implement the rotation vector,
linear acceleration, and gravity sensors without using the gyroscope.</p>

<table>
  <tr>
    <th width=34%>Sensor type</th>
    <th width=10%>Category</th>
    <th width=34%>Underlying physical sensors</th>
    <th width=19%>Reporting mode</th>
  </tr>
  <tr>
    <td><p><a href="#game_rotation_vector">Game rotation vector</a></p></td>
    <td><p>Attitude</p></td>
    <td><p>Accelerometer, Gyroscope MUST NOT USE Magnetometer</p></td>
    <td><p>Continuous</p></td>
  </tr>
  <tr>
    <td><p><a href="#geomagnetic_rotation_vector">Geomagnetic rotation
     vector</a> <img src="images/battery_icon.png" width="20" height="20" alt="Low
     power sensor" /></p></td>
    <td><p>Attitude</p></td>
    <td><p>Accelerometer, Magnetometer, MUST NOT USE Gyroscope</p></td>
    <td><p>Continuous</p></td>
  </tr>
  <tr>
    <td><a href="#glance_gesture">Glance gesture</a> <img
     src="images/battery_icon.png" width="20" height="20" alt="Low power sensor"
     /></p></td>
    <td><p>Interaction</p></td>
    <td><p>Undefined</p></td>
    <td><p>One-shot</p></td>
  </tr>
  <tr>
    <td><p><a href="#gravity">Gravity</a></p></td>
    <td><p>Attitude</p></td>
    <td><p>Accelerometer, Gyroscope</p></td>
    <td><p>Continuous</p></td>
  </tr>
  <tr>
    <td><p><a href="#gyroscope_uncalibrated">Gyroscope uncalibrated</a></p></td>
    <td><p>Uncalibrated</p></td>
    <td><p>Gyroscope</p></td>
    <td><p>Continuous</p></td>
  </tr>
  <tr>
    <td><p><a href="#linear_acceleration">Linear acceleration</a></p></td>
    <td><p>Activity</p></td>
    <td><p>Accelerometer, Gyroscope (if present) or Magnetometer (if gyro not present)</p></td>
    <td><p>Continuous</p></td>
  </tr>
  <tr>
    <td><p><a href="#magnetic_field_uncalibrated">Magnetic field uncalibrated</a></p></td>
    <td><p>Uncalibrated</p></td>
    <td><p>Magnetometer</p></td>
    <td><p>Continuous</p></td>
  </tr>
  <tr>
    <td><p><a href="#orientation_deprecated">Orientation</a> (deprecated)</p></td>
    <td><p>Attitude</p></td>
    <td><p>Accelerometer, Magnetometer PREFERRED Gyroscope</p></td>
    <td><p>Continuous</p></td>
  </tr>
  <tr>
    <td><p><a href="#pick_up_gesture">Pick up gesture</a> <img
     src="images/battery_icon.png" width="20" height="20" alt="Low power sensor"
     /></p></td>
    <td><p>Interaction</p></td>
    <td><p>Undefined</p></td>
    <td><p>One-shot</p></td>
  </tr>
  <tr>
    <td><p><a href="#rotation_vector">Rotation vector</a></p></td>
    <td><p>Attitude</p></td>
    <td><p>Accelerometer, Magnetometer, AND (when present) <em>Gyroscope </em></p></td>
    <td><p>Continuous</p></td>
  </tr>
  <tr>
    <td><p><a href="#significant_motion">Significant motion</a> <img
     src="images/battery_icon.png" width="20" height="20" alt="Low power sensor"
     /></p></td>
    <td><p>Activity</p></td>
    <td><p>Accelerometer (or another as long as very low power)</p></td>
    <td><p>One-shot</p></td>
  </tr>
  <tr>
    <td><p><a href="#step_counter">Step counter</a> <img
     src="images/battery_icon.png" width="20" height="20" alt="Low power sensor"
     /></p></td>
    <td><p>Activity</p></td>
    <td><p>Accelerometer</p></td>
    <td><p>On-change</p></td>
  </tr>
  <tr>
    <td><p><a href="#step_detector">Step detector</a> <img
     src="images/battery_icon.png" width="20" height="20" alt="Low power sensor"
     /></p></td> <td><p>Activity</p></td>
    <td><p>Accelerometer</p></td>
    <td><p>Special</p></td>
  </tr>
  <tr>
    <td><p><a href="#tilt_detector">Tilt detector</a> <img
     src="images/battery_icon.png" width="20" height="20" alt="Low power sensor"
     /></p></td>
    <td><p>Activity</p></td>
    <td><p>Accelerometer</p></td>
    <td><p>Special</p></td>
  </tr>
  <tr>
    <td><p><a href="#wake_up_gesture">Wake up gesture</a> <img
     src="images/battery_icon.png" width="20" height="20" alt="Low power sensor"
     /></p></td>
    <td><p>Interaction</p></td>
    <td><p>Undefined</p></td>
    <td><p>One-shot</p></td>
  </tr>
</table>
<p><img src="images/battery_icon.png" width="20" height="20" alt="Low power sensor" /> = Low power sensor</p>
<h2 id="activity_composite_sensors">Activity composite sensors</h2>
<h3 id="linear_acceleration">Linear acceleration</h3>
<p>Underlying physical sensors:  Accelerometer and (if present) Gyroscope (or
  magnetometer if gyroscope not present)</p>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_LINEAR_ACCELERATION)</code> <em>returns a non-wake-up sensor</em></p>
<p>A linear acceleration sensor reports the linear acceleration of the device in
  the sensor frame, not including gravity.</p>
<p>The output is conceptually: output of the <a
  href="#accelerometer">accelerometer</a> minus the output of the <a
  href="#gravity">gravity sensor</a>. It is reported in m/s^2 in the x, y and z
  fields of <code>sensors_event_t.acceleration</code>.</p>
<p>Readings on all axes should be close to 0 when the device is immobile.</p>
<p>If the device possesses a gyroscope, the linear acceleration sensor must use
  the gyroscope and accelerometer as input.</p>
<p>If the device doesn’t possess a gyroscope, the linear acceleration sensor must
  use the accelerometer and the magnetometer as input.</p>
<h3 id="significant_motion">Significant motion</h3>
<p>Underlying physical sensor: Accelerometer (or another as long as low power)</p>
<p>Reporting-mode: <em><a href="report-modes.html#one-shot">One-shot</a></em></p>
<p>Low-power</p>
<p>Implement only the wake-up version of this sensor.</p>
<p><code>getDefaultSensor(SENSOR_TYPE_SIGNIFICANT_MOTION)</code> <em>returns a wake-up sensor</em></p>
<p>A significant motion detector triggers when the detecting a “significant
  motion”: a motion that might lead to a change in the user location.</p>
<p>Examples of such significant motions are:</p>
<ul>
  <li> walking or biking </li>
  <li> sitting in a moving car, coach or train </li>
</ul>
<p>Examples of situations that do not trigger significant motion:</p>
<ul>
  <li> phone in pocket and person is not moving </li>
  <li> phone is on a table and the table shakes a bit due to nearby traffic or washing
    machine </li>
</ul>
<p>At the high level, the significant motion detector is used to reduce the power
  consumption of location determination. When the localization algorithms detect
  that the device is static, they can switch to a low power mode, where they rely
  on significant motion to wake the device up when the user is changing location.</p>
<p>This sensor must be low power. It makes a tradeoff for power consumption that
  may result in a small amount of false negatives. This is done for a few
  reasons:</p>
<ul>
  <li> The goal of this sensor is to save power. </li>
  <li> Triggering an event when the user is not moving (false positive) is costly in
    terms of power, so it should be avoided. </li>
  <li> Not triggering an event when the user is moving (false negative) is acceptable
    as long as it is not done repeatedly. If the user has been walking for 10
    seconds, not triggering an event within those 10 seconds is not acceptable. </li>
</ul>
<p>Each sensor event reports 1 in <code>sensors_event_t.data[0]</code></p>
<h3 id="step_detector">Step detector</h3>
<p>Underlying physical sensor: Accelerometer (+ possibly others as long as low
  power)</p>
<p>Reporting-mode: <em><a href="report-modes.html#special">Special</a> (one event per step taken)</em></p>
<p>Low-power</p>
<p><code>getDefaultSensor(SENSOR_TYPE_STEP_DETECTOR)</code> <em>returns a non-wake-up sensor</em></p>
<p>A step detector generates an event each time a step is taken by the user.</p>
<p>The timestamp of the event <code>sensors_event_t.timestamp</code> corresponds to when the
  foot hit the ground, generating a high variation in acceleration.</p>
<p>Compared to the step counter, the step detector should have a lower latency
  (less than 2 seconds). Both the step detector and the step counter detect when
  the user is walking, running and walking up the stairs. They should not trigger
  when the user is biking, driving or in other vehicles.</p>
<p>This sensor must be low power. That is, if the step detection cannot be done in
  hardware, this sensor should not be defined. In particular, when the step
  detector is activated and the accelerometer is not, only steps should trigger
  interrupts (not every accelerometer reading).</p>
<p><code>sampling_period_ns</code> has no impact on step detectors.</p>
<p>Each sensor event reports 1 in <code>sensors_event_t.data[0]</code></p>
<h3 id="step_counter">Step counter</h3>
<p>Underlying physical sensor: Accelerometer (+ possibly others as long as low
  power)</p>
<p>Reporting-mode: <em><a href="report-modes.html#on-change">On-change</a></em></p>
<p>Low-power</p>
<p><code>getDefaultSensor(SENSOR_TYPE_STEP_COUNTER)</code> <em>returns a non-wake-up sensor</em></p>
<p>A step counter reports the number of steps taken by the user since the last
  reboot while activated.</p>
<p>The measurement is reported as a <code>uint64_t</code> in
  <code>sensors_event_t.step_counter</code> and
  is reset to zero only on a system reboot.</p>
<p>The timestamp of the event is set to the time when the last step for that event
  was taken.</p>
<p>See the <a href="#step_detector">Step detector</a> sensor type for the signification of the time of a step.</p>
<p>Compared to the step detector, the step counter can have a higher latency (up
  to 10 seconds). Thanks to this latency, this sensor has a high accuracy; the
  step count after a full day of measures should be within 10% of the actual step
  count. Both the step detector and the step counter detect when the user is
  walking, running and walking up the stairs. They should not trigger when the
  user is biking, driving or in other vehicles.</p>
<p>The hardware must ensure the internal step count never overflows. The minimum
  size of the hardware's internal counter shall be 16 bits. In case of imminent
  overflow (at most every ~2^16 steps), the SoC can be woken up so the driver can
  do the counter maintenance.</p>
<p>As stated in <a href="interaction.html">Interaction</a>, while this sensor
  operates, it shall not disrupt any other sensors, in particular, the
  accelerometer, which might very well be in use.</p>
<p>If a particular device cannot support these modes of operation, then this
  sensor type must not be reported by the HAL. ie: it is not acceptable to
  &quot;emulate&quot; this sensor in the HAL.</p>
<p>This sensor must be low power. That is, if the step detection cannot be done in
  hardware, this sensor should not be defined. In particular, when the step
  counter is activated and the accelerometer is not, only steps should trigger
  interrupts (not accelerometer data).</p>
<h3 id="tilt_detector">Tilt detector</h3>
<p>Underlying physical sensor: Accelerometer (+ possibly others as long as low
  power)</p>
<p>Reporting-mode: <em><a href="report-modes.html#special">Special</a></em></p>
<p>Low-power</p>
<p>Implement only the wake-up version of this sensor.</p>
<p><code>getDefaultSensor(SENSOR_TYPE_TILT_DETECTOR)</code> <em>returns a wake-up sensor</em></p>
<p>A tilt detector generates an event each time a tilt event is detected.</p>
<p>A tilt event is defined by the direction of the 2-seconds window average
  gravity changing by at least 35 degrees since the activation or the last event
  generated by the sensor. Here is the algorithm:</p>
<ul>
  <li> <code>reference_estimated_gravity</code> = average of accelerometer measurements over the
    first second after activation or the estimated gravity when the last tilt event
    was generated. </li>
  <li> <code>current_estimated_gravity</code> = average of accelerometer measurements over the last
    2 seconds. </li>
  <li> trigger when <code>angle(reference_estimated_gravity, current_estimated_gravity) &gt; 35
    degrees</code> </li>
</ul>
<p>Large accelerations without a change in phone orientation should not trigger a
  tilt event. For example, a sharp turn or strong acceleration while driving a
  car should not trigger a tilt event, even though the angle of the average
  acceleration might vary by more than 35 degrees.
  Typically, this sensor is
  implemented with the help of only an accelerometer. Other sensors can be used
  as well if they do not increase the power consumption significantly. This is a
  low power sensor that should allow the SoC to go into suspend mode. Do not
  emulate this sensor in the HAL. Each sensor event reports 1 in
  <code>sensors_event_t.data[0]</code>.</p>
<h2 id="attitude_composite_sensors">Attitude composite sensors</h2>
<h3 id="rotation_vector">Rotation vector</h3>
<p>Underlying physical sensors: Accelerometer, Magnetometer, and Gyroscope</p>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_ROTATION_VECTOR)</code> <em>returns a non-wake-up sensor</em></p>
<p>A rotation vector sensor reports the orientation of the device relative to the
  East-North-Up coordinates frame. It is usually obtained by integration of
  accelerometer, gyroscope, and magnetometer readings. The East-North-Up
  coordinate system is defined as a direct orthonormal basis where:</p>
<ul>
  <li> X points east and is tangential to the ground. </li>
  <li> Y points north and is tangential to the ground. </li>
  <li> Z points towards the sky and is perpendicular to the ground. </li>
</ul>
<p>The orientation of the phone is represented by the rotation necessary to align
  the East-North-Up coordinates with the phone's coordinates. That is, applying
  the rotation to the world frame (X,Y,Z) would align them with the phone
  coordinates (x,y,z).</p>
<p>The rotation can be seen as rotating the phone by an angle theta around an axis
  rot_axis to go from the reference (East-North-Up aligned) device orientation to
  the current device orientation. The rotation is encoded as the four unit-less
  x, y, z, w components of a unit quaternion:</p>
<ul>
  <li> <code>sensors_event_t.data[0] = rot_axis.x*sin(theta/2)</code> </li>
  <li> <code>sensors_event_t.data[1] = rot_axis.y*sin(theta/2)</code> </li>
  <li> <code>sensors_event_t.data[2] = rot_axis.z*sin(theta/2)</code> </li>
  <li> <code>sensors_event_t.data[3] = cos(theta/2)</code> </li>
</ul>
<p>Where:</p>
<ul>
  <li> the  x, y and z fields of <code>rot_axis</code> are the East-North-Up
  coordinates of a unit length vector representing the rotation axis </li>
  <li> <code>theta</code> is the rotation angle </li>
</ul>
<p>The quaternion is a unit quaternion: it must be of norm 1. Failure to ensure
  this will cause erratic client behaviour.</p>
<p>In addition, this sensor reports an estimated heading accuracy:</p>
<p><code>sensors_event_t.data[4] = estimated_accuracy</code> (in radians)</p>
<p>The heading error must be less than <code>estimated_accuracy</code> 95% of
the time. This sensor must use a gyroscope as the main orientation change input.</p>
<p>This sensor also uses accelerometer and magnetometer input to make up
  for gyroscope drift, and it cannot be implemented using only the accelerometer
  and magnetometer.</p>
<h3 id="game_rotation_vector">Game rotation vector</h3>
<p>Underlying physical sensors: Accelerometer and Gyroscope (no Magnetometer)</p>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_GAME_ROTATION_VECTOR)</code> <em>returns a non-wake-up sensor</em></p>
<p>A game rotation vector sensor is similar to a rotation vector sensor but not
  using 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 drifts around the Z axis.</p>
<p>See the <a href="#rotation_vector">Rotation vector</a> sensor for details on
  how to set <code>sensors_event_t.data[0-3]</code>. This sensor does
  not report an estimated heading accuracy:
  <code>sensors_event_t.data[4]</code> is reserved and should be set to 0.</p>
<p>In an ideal case, a phone rotated and returned to the same real-world
  orientation should report the same game rotation vector.</p>
<p>This sensor must be based on a gyroscope and an accelerometer. It cannot use
  magnetometer as an input, besides, indirectly, through estimation of the
  gyroscope bias.</p>
<h3 id="gravity">Gravity</h3>
<p>Underlying physical sensors: Accelerometer and (if present) Gyroscope (or
  magnetometer if gyroscope not present)</p>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_GRAVITY)</code> <em>returns a non-wake-up sensor</em></p>
<p>A gravity sensor reports the direction and magnitude of gravity in the device's
  coordinates.</p>
<p>The gravity vector components are reported in m/s^2 in the x, y and z fields of
  <code>sensors_event_t.acceleration</code>.</p>
<p>When the device is at rest, the output of the gravity sensor should be
  identical to that of the accelerometer. On Earth, the magnitude is around 9.8
  m/s^2.</p>
<p>If the device possesses a gyroscope, the gravity sensor must use the
  gyroscope and accelerometer as input.</p>
<p>If the device doesn’t possess a gyroscope, the gravity sensor must use the
  accelerometer and the magnetometer as input.</p>
<h3 id="geomagnetic_rotation_vector">Geomagnetic rotation vector</h3>
<p>Underlying physical sensors: Accelerometer and Magnetometer (no Gyroscope)</p>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p>Low-power</p>
<p><code>getDefaultSensor(SENSOR_TYPE_GEOMAGNETIC_ROTATION_VECTOR)</code> <em>returns a non-wake-up sensor</em></p>
<p>A geomagnetic rotation vector is similar to a rotation vector sensor but using
  a magnetometer and no gyroscope.</p>
<p>This sensor must be based on a magnetometer. It cannot be implemented using a
  gyroscope, and gyroscope input cannot be used by this sensor.</p>
<p>See the <a href="#rotation_vector">Rotation vector</a> sensor for details on
  how to set <code>sensors_event_t.data[0-4]</code>.</p>
<p>Just like for the rotation vector sensor, the heading error must be less than
  the estimated accuracy (<code>sensors_event_t.data[4]</code>) 95% of the time.</p>
<p>This sensor must be low power, so it has to be implemented in hardware.</p>
<h3 id="orientation_deprecated">Orientation (deprecated)</h3>
<p>Underlying physical sensors: Accelerometer, Magnetometer and (if present)
  Gyroscope</p>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_ORIENTATION)</code> <em>returns a non-wake-up sensor</em></p>
<p class="note"><strong>Note:</strong> This is an older sensor type that has been deprecated in the Android SDK.
  It has been replaced by the rotation vector sensor, which is more clearly
  defined. Use the rotation vector sensor over the orientation sensor whenever
  possible.</p>
<p>An orientation sensor reports the attitude of the device. The measurements are
  reported in degrees in the x, y and z fields of <code>sensors_event_t.orientation</code>:</p>
<ul>
  <li> <code>sensors_event_t.orientation.x</code>: azimuth, the angle between the magnetic north
    direction and the Y axis, around the Z axis (<code>0&lt;=azimuth&lt;360</code>). 0=North, 90=East,
    180=South, 270=West </li>
  <li> <code>sensors_event_t.orientation.y</code>: pitch, rotation around X axis
    (<code>-180&lt;=pitch&lt;=180</code>), with positive values when the z-axis moves toward the
    y-axis. </li>
  <li> <code>sensors_event_t.orientation.z</code>: roll, rotation around Y axis (<code>-90&lt;=roll&lt;=90</code>),
    with positive values when the x-axis moves towards the z-axis. </li>
</ul>
<p>Please note, for historical reasons the roll angle is positive in the clockwise
  direction. (Mathematically speaking, it should be positive in the
  counter-clockwise direction):</p>
<div class="figure" style="width:264px">
  <img src="images/axis_positive_roll.png" alt="Depiction of orientation
   relative to a device" height="253" />
  <p class="img-caption">
    <strong>Figure 3.</strong> Orientation relative to a device.
  </p>
</div>
<p>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).</p>
<p>The orientation sensor also reports how accurate it expects its readings to be
  through sensors_event_t.orientation.status. See the <a href="http://developer.android.com/reference/android/hardware/SensorManager.html">SensorManager</a>’s <a href="http://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH">SENSOR_STATUS_</a>* constants for more information on possible values for this field.</p>
<h2 id="uncalibrated_sensors">Uncalibrated sensors</h2>
<p>Uncalibrated sensors provide more raw results and may include some bias but
  also contain fewer &quot;jumps&quot; from corrections applied through calibration. Some
  applications may prefer these uncalibrated results as smoother and more
  reliable. For instance, if an application is attempting to conduct its own
  sensor fusion, introducing calibrations can actually distort results.</p>
<h3 id="gyroscope_uncalibrated">Gyroscope uncalibrated</h3>
<p>Underlying physical sensor: Gyroscope</p>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_GYROSCOPE_UNCALIBRATED)</code> <em>returns a non-wake-up sensor</em></p>
<p>An uncalibrated gyroscope reports the rate of rotation around the sensor axes
  without applying bias compensation to them, along with a bias estimate. All
  values are in radians/second and are reported in the fields of
  <code>sensors_event_t.uncalibrated_gyro</code>:</p>
<ul>
  <li> <code>x_uncalib</code>: angular speed (w/o drift compensation) around the X axis </li>
  <li> <code>y_uncalib</code>: angular speed (w/o drift compensation) around the Y axis </li>
  <li> <code>z_uncalib</code>: angular speed (w/o drift compensation) around the Z axis </li>
  <li> <code>x_bias</code>: estimated drift around X axis </li>
  <li> <code>y_bias</code>: estimated drift around Y axis </li>
  <li> <code>z_bias</code>: estimated drift around Z axis </li>
</ul>
<p>Conceptually, the uncalibrated measurement is the sum of the calibrated
  measurement and the bias estimate: <code>_uncalibrated = _calibrated + _bias</code>.</p>
<p>The <code>x/y/z_bias</code> values are expected to jump as soon as the estimate of the bias
  changes, and they should be stable the rest of the time.</p>
<p>See the definition of the <a href="#gyroscope">gyroscope</a> sensor for
  details on the coordinate system used.</p>
<p>Factory calibration and temperature compensation must be applied to the
  measurements. Also, gyroscope drift estimation must be implemented so that
  reasonable estimates can be reported in <code>x_bias</code>,
  <code>y_bias</code> and <code>z_bias</code>. If the
  implementation is not able to estimate the drift, then this sensor must not be
  implemented.</p>
<p>If this sensor is present, then the corresponding Gyroscope sensor must also be
  present and both sensors must share the same <code>sensor_t.name</code> and
  <code>sensor_t.vendor</code> values.</p>
<h3 id="magnetic_field_uncalibrated">Magnetic field uncalibrated</h3>
<p>Underlying physical sensor: Magnetometer</p>
<p>Reporting-mode: <em><a href="report-modes.html#continuous">Continuous</a></em></p>
<p><code>getDefaultSensor(SENSOR_TYPE_MAGNETIC_FIELD_UNCALIBRATED)</code> <em>returns a non-wake-up sensor</em></p>
<p>An uncalibrated magnetic field sensor reports the ambient magnetic field
  together with a hard iron calibration estimate. All values are in micro-Tesla
  (uT) and are reported in the fields of <code>sensors_event_t.uncalibrated_magnetic</code>:</p>
<ul>
  <li> <code>x_uncalib</code>: magnetic field (w/o hard-iron compensation) along the X axis </li>
  <li> <code>y_uncalib</code>: magnetic field (w/o hard-iron compensation) along the Y axis </li>
  <li> <code>z_uncalib</code>: magnetic field (w/o hard-iron compensation) along the Z axis </li>
  <li> <code>x_bias</code>: estimated hard-iron bias along the X axis </li>
  <li> <code>y_bias</code>: estimated hard-iron bias along the Y axis </li>
  <li> <code>z_bias</code>: estimated hard-iron bias along the Z axis </li>
</ul>
<p>Conceptually, the uncalibrated measurement is the sum of the calibrated
  measurement and the bias estimate: <code>_uncalibrated = _calibrated + _bias</code>.</p>
<p>The uncalibrated magnetometer allows higher level algorithms to handle bad hard
  iron estimation. The <code>x/y/z_bias</code> values are expected to jump as soon as the
  estimate of the hard-iron changes, and they should be stable the rest of the
  time.</p>
<p>Soft-iron calibration and temperature compensation must be applied to the
  measurements. Also, hard-iron estimation must be implemented so that reasonable
  estimates can be reported in <code>x_bias</code>, <code>y_bias</code> and
  <code>z_bias</code>. If the implementation is not able to estimate the bias,
  then this sensor must not be implemented.</p>
<p>If this sensor is present, then the corresponding magnetic field sensor must be
  present and both sensors must share the same <code>sensor_t.name</code> and
  <code>sensor_t.vendor</code> values.</p>
<h2 id="interaction_composite_sensors">Interaction composite sensors</h2>
<p>Some sensors are mostly used to detect interactions with the user. We do not
  define how those sensors must be implemented, but they must be low power and it
  is the responsibility of the device manufacturer to verify their quality in
  terms of user experience.</p>
<h3 id="wake_up_gesture">Wake up gesture</h3>
<p>Underlying physical sensors: Undefined (anything low power)</p>
<p>Reporting-mode: <em><a href="report-modes.html#one-shot">One-shot</a></em></p>
<p>Low-power</p>
<p>Implement only the wake-up version of this sensor.</p>
<p><code>getDefaultSensor(SENSOR_TYPE_WAKE_GESTURE)</code> <em>returns a wake-up sensor</em></p>
<p>A wake up gesture sensor enables waking up the device based on a device
  specific motion. When this sensor triggers, the device behaves as if the power
  button was pressed, turning the screen on. This behavior (turning on the screen
  when this sensor triggers) might be deactivated by the user in the device
  settings. Changes in settings do not impact the behavior of the sensor: only
  whether the framework turns the screen on when it triggers.
  The actual gesture to be detected is not specified, and can be chosen by the
  manufacturer of the device.</p>
<p>This sensor must be low power, as it is likely to be activated 24/7.</p>
<p>Each sensor event reports 1 in <code>sensors_event_t.data[0]</code>.</p>
<h3 id="pick_up_gesture">Pick up gesture</h3>
<p>Underlying physical sensors: Undefined (anything low power)</p>
<p>Reporting-mode: <em><a href="report-modes.html#one-shot">One-shot</a></em></p>
<p>Low-power</p>
<p>Implement only the wake-up version of this sensor.</p>
<p><code>getDefaultSensor(SENSOR_TYPE_PICK_UP_GESTURE)</code> <em>returns a wake-up sensor</em></p>
<p>A pick-up gesture sensor triggers when the device is picked up
  regardless of wherever it was before (desk, pocket, bag).</p>
<p>Each sensor event reports 1 in <code>sensors_event_t.data[0]</code>.</p>
<h3 id="glance_gesture">Glance gesture</h3>
<p>Underlying physical sensors: Undefined (anything low power)</p>
<p>Reporting-mode: <em><a href="report-modes.html#one-shot">One-shot</a></em></p>
<p>Low-power</p>
<p>Implement only the wake-up version of this sensor.</p>
<p><code>getDefaultSensor(SENSOR_TYPE_GLANCE_GESTURE)</code> <em>returns a wake-up sensor</em></p>
<p>A glance gesture sensor enables briefly turning the screen on to enable the
  user to glance content on screen based on a specific motion. When this sensor
  triggers, the device will turn the screen on momentarily to allow the user to
  glance notifications or other content while the device remains locked in a
  non-interactive state (dozing), then the screen will turn off again. This
  behavior (briefly turning on the screen when this sensor triggers) might be
  deactivated by the user in the device settings. Changes in settings do not
  impact the behavior of the sensor: only whether the framework briefly turns the
  screen on when it triggers. The actual gesture to be detected is not specified,
  and can be chosen by the manufacturer of the device.</p>
<p>This sensor must be low power, as it is likely to be activated 24/7.
  Each sensor event reports 1 in <code>sensors_event_t.data[0]</code>.</p>