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Most Android-powered devices have an accelerometer, and many now include a gyroscope. The availability of the software-based sensors is more variable because they often rely on one or more hardware sensors to derive their data. Depending on the device, these software-based sensors can derive their data either from the accelerometer and magnetometer or from the gyroscope.

Motion sensors are useful for monitoring device movement, such as tilt, shake, rotation, or swing. The movement is usually a reflection of direct user input for example, a user steering a car in a game or a user controlling a ball in a game , but it can also be a reflection of the physical environment in which the device is sitting for example, moving with you while you drive your car.

In the first case, you are monitoring motion relative to the device’s frame of reference or your application’s frame of reference; in the second case you are monitoring motion relative to the world’s frame of reference. Motion sensors by themselves are not typically used to monitor device position, but they can be used with other sensors, such as the geomagnetic field sensor, to determine a device’s position relative to the world’s frame of reference see Position Sensors for more information.

All of the motion sensors return multi-dimensional arrays of sensor values for each SensorEvent. For example, during a single sensor event the accelerometer returns acceleration force data for the three coordinate axes, and the gyroscope returns rate of rotation data for the three coordinate axes. These data values are returned in a float array values along with other SensorEvent parameters.

Table 1 summarizes the motion sensors that are available on the Android platform. Table 1. Motion sensors that are supported on the Android platform. The rotation vector sensor and the gravity sensor are the most frequently used sensors for motion detection and monitoring. The rotational vector sensor is particularly versatile and can be used for a wide range of motion-related tasks, such as detecting gestures, monitoring angular change, and monitoring relative orientation changes.

For example, the rotational vector sensor is ideal if you are developing a game, an augmented reality application, a 2-dimensional or 3-dimensional compass, or a camera stabilization app.

In most cases, using these sensors is a better choice than using the accelerometer and geomagnetic field sensor or the orientation sensor. The Android Open Source Project AOSP provides three software-based motion sensors: a gravity sensor, a linear acceleration sensor, and a rotation vector sensor. These sensors were updated in Android 4. If you want to try these sensors, you can identify them by using the getVendor method and the getVersion method the vendor is Google LLC; the version number is 3.

Identifying these sensors by vendor and version number is necessary because the Android system considers these three sensors to be secondary sensors. For example, if a device manufacturer provides their own gravity sensor, then the AOSP gravity sensor shows up as a secondary gravity sensor. All three of these sensors rely on a gyroscope: if a device does not have a gyroscope, these sensors do not show up and are not available for use. The gravity sensor provides a three dimensional vector indicating the direction and magnitude of gravity.

Typically, this sensor is used to determine the device’s relative orientation in space. The following code shows you how to get an instance of the default gravity sensor:.

Note: When a device is at rest, the output of the gravity sensor should be identical to that of the accelerometer. The linear acceleration sensor provides you with a three-dimensional vector representing acceleration along each device axis, excluding gravity. You can use this value to perform gesture detection. The value can also serve as input to an inertial navigation system, which uses dead reckoning.

The following code shows you how to get an instance of the default linear acceleration sensor:. Conceptually, this sensor provides you with acceleration data according to the following relationship:. You typically use this sensor when you want to obtain acceleration data without the influence of gravity. For example, you could use this sensor to see how fast your car is going. The linear acceleration sensor always has an offset, which you need to remove. The simplest way to do this is to build a calibration step into your application.

During calibration you can ask the user to set the device on a table, and then read the offsets for all three axes.

You can then subtract that offset from the acceleration sensor’s direct readings to get the actual linear acceleration. The following code shows you how to get an instance of the default rotation vector sensor:. Figure 1. Coordinate system used by the rotation vector sensor. Elements of the rotation vector are unitless. The x, y, and z axes are defined in the same way as the acceleration sensor. The reference coordinate system is defined as a direct orthonormal basis see figure 1.

This coordinate system has the following characteristics:. For a sample application that shows how to use the rotation vector sensor, see RotationVectorDemo.

The significant motion sensor triggers an event each time significant motion is detected and then it disables itself. A significant motion is a motion that might lead to a change in the user’s location; for example walking, biking, or sitting in a moving car. The following code shows you how to get an instance of the default significant motion sensor and how to register an event listener:.

For more information, see TriggerEventListener. The step counter sensor provides the number of steps taken by the user since the last reboot while the sensor was activated. The step counter has more latency up to 10 seconds but more accuracy than the step detector sensor. To preserve the battery on devices running your app, you should use the JobScheduler class to retrieve the current value from the step counter sensor at a specific interval.

Although different types of apps require different sensor-reading intervals, you should make this interval as long as possible unless your app requires real-time data from the sensor. The step detector sensor triggers an event each time the user takes a step. The latency is expected to be below 2 seconds. The following sensors provide your app with raw data about the linear and rotational forces being applied to the device. In order to use the values from these sensors effectively, you need to filter out factors from the environment, such as gravity.

You might also need to apply a smoothing algorithm to the trend of values to reduce noise. An acceleration sensor measures the acceleration applied to the device, including the force of gravity. The following code shows you how to get an instance of the default acceleration sensor:. Conceptually, an acceleration sensor determines the acceleration that is applied to a device A d by measuring the forces that are applied to the sensor itself F s using the following relationship:.

However, the force of gravity is always influencing the measured acceleration according to the following relationship:. Similarly, when the device is in free fall and therefore rapidly accelerating toward the ground at 9. Therefore, to measure the real acceleration of the device, the contribution of the force of gravity must be removed from the accelerometer data.

This can be achieved by applying a high-pass filter. Conversely, a low-pass filter can be used to isolate the force of gravity. The following example shows how you can do this:. Note: You can use many different techniques to filter sensor data. The code sample above uses a simple filter constant alpha to create a low-pass filter. This filter constant is derived from a time constant t , which is a rough representation of the latency that the filter adds to the sensor events, and the sensor’s event delivery rate dt.

The code sample uses an alpha value of 0. If you use this filtering method you may need to choose a different alpha value. Accelerometers use the standard sensor coordinate system. In practice, this means that the following conditions apply when a device is laying flat on a table in its natural orientation:. In general, the accelerometer is a good sensor to use if you are monitoring device motion.

Almost every Android-powered handset and tablet has an accelerometer, and it uses about 10 times less power than the other motion sensors. One drawback is that you might have to implement low-pass and high-pass filters to eliminate gravitational forces and reduce noise. The following code shows you how to get an instance of the default gyroscope:.

The sensor’s coordinate system is the same as the one 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.

This is the standard mathematical definition of positive rotation and is not the same as the definition for roll that is used by the orientation sensor.

Usually, the output of the gyroscope is integrated over time to calculate a rotation describing the change of angles over the timestep. For example:. Standard gyroscopes provide raw rotational data without any filtering or correction for noise and drift bias.

In practice, gyroscope noise and drift will introduce errors that need to be compensated for. You usually determine the drift bias and noise by monitoring other sensors, such as the gravity sensor or accelerometer. The uncalibrated gyroscope is similar to the gyroscope , except that no gyro-drift compensation is applied to the rate of rotation. Factory calibration and temperature compensation are still applied to the rate of rotation. The uncalibrated gyroscope is useful for post-processing and melding orientation data.

That is,. Note: Uncalibrated sensors provide more raw results and may include some bias, but their measurements contain fewer jumps 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. In addition to the rates of rotation, the uncalibrated gyroscope also provides the estimated drift around each axis.

The following code shows you how to get an instance of the default uncalibrated gyroscope:. Content and code samples on this page are subject to the licenses described in the Content License. App Basics. Build your first app.