This document discusses using accelerometer and gyroscope sensor fusion to improve motion control. It begins by reviewing a previous presentation on using only an accelerometer for motion recognition. It then describes how each sensor - accelerometer, gyroscope, and compass - measures motion differently, with strengths and weaknesses. The main idea is to use a gyroscope to compensate for the gravity component detected by the accelerometer, allowing separation of gravity from motion acceleration. This allows more accurate motion recognition compared to using just acceleration. Implementation examples and conclusions are provided on potential applications and approaches to sensor fusion.

1. Better motion control using
accelerometer/gyroscope sensor fusion
Gabor Paller
gaborpaller@gmail.com
Sfonge Ltd.
http://www.sfonge.com

2. Where were we?
● Droidcon 2011, London: Motion recognition on
Android devices
● http://mylifewithandroid.blogspot.com/2011/10/my-
presentation-about-motion.html
● Processing only the accelerometer for motion
recognition

3. Acceleration
Acceleration caused by Acceleration caused by the change
the change of direction of velocity
v1 dV
dV
v2
v2
v1
ΔV
a=
Δt

4. Extract motion information from
accelerometer data
● Accelerometer data is a vector, having 3 axes (x,y,z)
● This vector has the following components:
● Gravity acceleration
– Pointing toward the center of the Earth
– Value of about 10 m/s2
– That's what we measure when the accelerometer is used to
calculate tilt
● Any other acceleration the device is subject to
– Added to the gravity acceleration
– “Disturbs” tilt measurement in gaming (swift movements cause
acceleration) – hence the reason for gyroscopes
– Can be used for movement detection

5. Measured acceleration

6. Absolute value
● x, y, z: acceleration vector components
● g – value of the gravity acceleration (can be
approximated as 10)
a= √ x +y +z −g
2 2 2

7. Snap – one way accelerating
Movement starts:
Movement ends: decelerating

8. Droidcon 2011 flashback
● Conclusions:
● Power consumption is a problem
● Some neat functionality can be implemented by doing
pattern recognition on the acceleration vector's absolute
value
● In general case the gravity and motion acceleration
components cannot be separated
● You can try to use an additional sensor like the gyro to help
the separation

9. Gyroscope
● Very new phenomenon as gyroscopes suitable for consumer
electronic devices appeared very recently
● First appearance: Wii Motion Plus accessory, 2009 June
● First Android smart phone: Nexus S (end of 2010)
● Pros:
● Not sensitive to gravity
● Cons:
● Currently supported only by high-end Android phones
● Drift problems (more about that later)

10. Compass
● Measures the device orientation wrt. the magnetic vector of the Earth
● This vector points toward the magnetic center of the Earth
– It has a component that points to the magnetic North pole – that's what we
use for orientation
– Beware of the z component! (also called magnetic inclination). If the device
is not held horizontally, the downward vector element influences the
measurement
● Pros:
● Can be used to deduce gravity, not sensitive to motion acceleration
● Widely available in Android devices
● Cons:
● Requires calibration
● Sensitive to metal objects, magnetic fields (e.g. electric motors)

11. This time it is gyroscope only

12. Gyroscope

13. Gyroscope measurement data
● Measures rotation around 3 axes
● More exactly: measures rotation speed (angular
velocity) around the axes
Δφ
v x=
Δt

14. Getting the rotation angle
● Get the angle difference
Δ φ=v x Δ t
● Get the absolute angle
φ ' =φ+Δ φ

15. Drift

16. Noise

17. Gyro as support sensor
● Because of accumulating error, gyro alone can
be rarely used
● But
● The accelerometer has no accumulated error but
has the gravity component problem
● The gyro has accumulated error but is not sensitive
to gravity
● Sensor fusion: the use of multiple sensors so
that they compensate each other's weaknesses

18. Accelerometer-gyro fusion
● The easy way
● Use the virtual sensors that calculate gravity and
linear acceleration from multiple sensors
● The hard way
● Process raw accelerometer and gyroscope data to
yield the motion information you need

19. Virtual sensors
Gravity and motion acceleration
deduced from the accelerometer
and the gyroscope
Roll/pitch/yaw from the compass
Drift-compensated gyroscope

20. Drift-compensated gyroscope

21. The hard way
● Why would you go the hard way?
● Sensor fusion co-processing provided by the phone
is not precise enough or can have undesirable
properties (like auto-calibration in Nexus S)
● Virtual sensors are not available (is there any such
case with gyro-equipped phone?)
● You would like to understand how it works and what
to expect from built-in sensor fusion
● Just for the fun of it :-)

22. What we want
● Remember: accelerometer measures the sum
of gravity and motion acceleration
● Kills two use cases:
● If you need device tilt, the motion acceleration
component corrupts the measurement
● If you want motion acceleration, it is impossible to
subtract the gravity acceleration in a general case
● Separate gravity and motion acceleration with
the help of the gyroscope

23. Idea

24. Idea in words
● Pick a reliable gravity vector measurement
(make sure that there's no motion then)
● If you detect motion (more about later), rotate
the previous gravity vector using the gyroscope
data and use it as gravity vector estimation
● Subtract this gravity vector estimation from the
measured acceleration – this yields the motion
acceleration

25. Updating the gravity vector
estimation
● The gravity vector estimation has to be updated
time to time as rotation angle errors accumulate
● If we detect an acceleration measurement
where there is no motion acceleration, we can
take it as new reliable gravity vector estimation
● Remember slide #7: if the absolute value of the
accelerometer output is close to the Earth's
gravity, we can assume that there's no motion
→ the gravity vector estimation can be updated
with the current accelerometer output

26. Implementation
● Example program:
http://www.sfonge.com/forum/topic/example-
application-accelerometergyroscope-
processing-android

27. Now what?
3D linear acceleration signal of a well-known motion

28. Recognizing motion
● 3D linear acceleration signals are not so
intuitive
● Motion recognition:
● Record acceleration pattern of reference motion
and compare with these references
● Convert from acceleration domain to something
more intuitive like velocity
– Accelerometer/gyroscope bias will become linearly
growing drift after you integrate the acceleration signal!

29. Walking with swinging hand

30. Walking with steady hand

31. Cutting corners

32. Conclusions
● Each sensor has strengths and weaknesses
● Combine them and they compensate each
other
● Some sensor fusion is already built-in
● If not → don't worry, come up with your own, it's
fun!
● Motion recognition based on 3D linear
acceleration signal is much more exact than
doing the same from 1D signal

33. Questions?