Inertial Measurement Units (IMUs) – Theory and Practice
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Transcript of Inertial Measurement Units (IMUs) – Theory and Practice
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IMU Tutorial 10.05.12 1
H.J. Sommer III, Ph.D.The Pennsylvania State University
University Park, PA [email protected]
www.mne.psu.edu/sommer
Inertial Measurement Units(IMUs) – Theory and Practice
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Kinematic measurements using inertial references
Attitude and magnetic heading Angular velocity Acceleration
Fuse data to provide more reliable results
Inertial Measurement Unit ?
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Inertial Measurement Unit ?
14x28 mm
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Inertial Measurement Unit ?
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Inertial Measurement Unit ?
hr
s
OP
OP
P
P
P OP+P
m, JP
2O
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Inertial Measurement Unit ?
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Inertial Measurement Unit ?
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Photogrammetry Absolute location of point markers
Goniometry Relative angles across body segments
Electromagnetic digitizers 6DOF of discrete sensors
Traditional KinematicMeasurements
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Photogrammetry
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Vanguard or RightGuard?
DLT or BLT?
Lo-Cam or Hi-Cam?
Photogrammetry Quiz(for Oldtimers)
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Positive Absolute location and attitude of body
segments Multiple IR cameras with ambient lighting Automatic marker tracking No cables to subject > 100 Hz, high resolution Markerless motion capture (MMC)
Photogrammetry
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Negative Calibration relative to anatomy (joints and
mass centers) Requires finite differences for velocity and
acceleration Marker occlusion Soft tissue artifact Limited workspace in a gait lab
Photogrammetry
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Goniometry
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Positive Direct measurement of joint motion Easy to use
Negative Does not measure absolute
position/attitude Physical attachment to subject
Goniometry
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Electromagnetic Digitizers
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Positive 6 DOF for each body segment
Negative Limited workspace Cables (new wireless) Physical attachment to subject Accuracy degraded by speed
Electromagnetic Digitizers
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IMUsIntegrated Kinematic Sensor (IKS) Wu and Ladin, 1993
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Attitude relative to gravity vector Magnetic heading Rotational velocity Translational acceleration
IMUs
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Positive Absolute attitude of body segments Direct measurement of angular velocity Direct measurement of acceleration No marker occlusion Large work space in unstructured
environment
IMUs
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Negative Does not provide absolute location,
translational velocity or rotational acceleration
Calibration relative to anatomy Soft tissue artifact Data communication < 100 Hz, medium resolution
IMUs
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Vehicle navigation Intercontinental ballistic missiles (ICBM) Nuclear submarines Cruise missiles
MicroElectroMechanical Systems (MEMS) Automotive Consumer products
History of IMUs
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Automotive Accelerometers to deploy airbags Vehicle roll handling
MEMS IMUs - Automotive
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Games (WiiMote) PDA (iPhone) Camera stabilization Hard disks
MEMS IMUs – Consumer Products
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MEMS Fabrication
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MEMS Comb Sensor/Drive
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acceleration
gravity
MEMS accelerometer(proof mass)
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MEMS accelerometer
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MEMS gyro (tuning fork)
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MEMS magnetometer (magnetoresistive)
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Signal Analog voltage (0 to 3V) Fixed frequency, variable duty cycle Digital (internal A/D converter)
Bandwidth < 150 Hz
MEMS IMU Outputs
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Biaxial accelerometer Uniaxial gyro
Two-Dimensional (2D) IMU
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Triaxial accelerometer Triaxial gyro Triaxial magnetometer
Required to determine spin about gravity vector
Three-Dimensional (3D) IMU
ax
ayaz
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Triaxial accelerometer ±3g, 300 mV/g, 550 Hz
Triaxial gyro ±300 deg/sec (dps), 3.3mV/dps, 140 Hz
Triaxial magnetometer 50 Hz
On-board CPU, serial I/O
MEMS 9DOF IMU
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Break Time
Stand upStretch
Say hello to your neighbor
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Sensor uncertainty Geometric
Rigid body Articulated model
State space Kalman filter
Data Fusion
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s = measured signal b = zero drift or bias (function of
temp) f = scale factor (function of temp) w = Gaussian white noise 2 = variance
Sensor Uncertainty
wsfb GYRO
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Nonlinearity ±1% b = 1.23 V, 0.05 degps/C° f = 300 degps/V, 0.05 %/C° = 0.035 degps/sqrt(Hz) pink noise
LSY530 gyro ±300 degps
wsfb GYRO
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Rigid Body Fusion
r/aa xCxD
axC
r
ayC
axD
ayD
C
D
Multiple IMUs per body Parallel axes Rejects gravity effects
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Articulated Model - Pendulum
2G
2G
PGmJ/sinPGgm
0sinPGgmPGmJ
pendulumsimple
axD
ayD
P
D
G
PD/singa
singPDa
xD
xD
PGPDfor0aroduniform
PGmJ
PDPGm1singa
34
xD
2G
xD
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Multiple Segment Model
1x27DR
REV
1x27
r
27x27DRrDR
REVrREV
q
q
6G/6RNK6G
6G26G10G/10RNK
10G10G
210G
8G/8RWR8G
8G28G9G/9RWR
9G9G
29G
7G/7REL7G
7G27G8G/8REL
8G8G
28G
6G/6RSL6G
6G26G7G/7RSL
7G7G
27G
5G/5RWA5G
5G25G6G/6RWA
6G6G
26G
4G/4RHP4G
4G24G5G/5RHP
5G5G
25G
3G/3RKN3G
3G23G4G/4RKN
4G4G
24G
2G/2RAN2G
2G22G3G/3RAN
3G3G
23G
2G/2RHL2G
2G22G
1x18REV
sAsA
sAsA
sAsA
sAsA
sAsA
sAsA
sAsA
sAsA
sA
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Uses state space model Position Velocity
Adaptive time domain filter Combines states Tracks variance-covariance Rejects zero drift
Kalman Filter
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Kalman Filter - 2D IMU
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Kalman Filter - Simplified
tt
ttt
tt
tttt
tt
toCompare
tCompute
toCompare
t/Compute
ttimeatandMeasure
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Kalman Filter – Prediction
latitude
probability
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Kalman Filter - Measurement
latitude
probability
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Kalman Filter - Correction
latitude
probability
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Kalman Filter - Prediction
latitude
probability
constant speed
fixed time
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Kalman Filter – 2D IMU
angle
probability
elmoddynamic
ttCORRPRED
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State space Include acceleration
Nonlinear state relationships ax-ay-dot versus dot
Include geometric multisegment model
Include states for multiple bodies
Kalman Filter - Extended
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Kalman Filter
kk
kkk
1k1kk
xofcovvarPtrack
vxHztsmeasuremenonbased
wxAxstateestimate
noisesensorv
matrixdynamicsensorH
noiseprocessw
matrixdynamicstateA
estimatepriorx
k
1k
1k
matrixgainadaptiveK
vofcovvarR
wofcovvarQ
k
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Kalman Filter
k
kk
k
PHKIP
xHzKxx
RHPHHPK
correction
APAP
xAx
prediction
kk
kkkkk
1TTk
Tk
1kk
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Stationary Simple attitude Simple motion Coordinated movement Inverse dynamics
Applications
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Minimal change in sensor orientation Hand/arm tremor
Extended arm, tracing spiral Triaxial accelerometer, >150 Hz
Postural sway Supracranial accelerometer Lumbar accelerometer
Stationary
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Body position during sleep Treatment for sleep apnea Triaxial accelerometer, very low sample rate Not interested in spin about gravity vector
Restless Leg Syndrome (RLS) Monitor sudden movement High frequency sample rate Interested in event itself, not characterization
Simple Attitude
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Planar lifting or reaching Simple articulated model 2D IMU provides position, velocity,
acceleration Passive manipulation or drop
Assess spasticity Compute jerk from acceleration
Simple Motion
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Basic assessment Triaxial accelerometer, >100 Hz Number of strides, timing Asymmetry of motion
Rehabilitation, prosthetic fitting
Full body motion Thirteen 9DOF IMUs Multiple segment model
Coordinated Movement
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2D Lower data throughput (3ch versus 9ch) Require sagittal and frontal IMUs Does not require magnetometers
3D Lifting or reaching most promising Difficulty in assessing absolute location of
feet
Inverse Dynamics
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Motion variables Consider alternate signals to describe
motion Number of IMUs
May require two per segment Synchronization
In-shoe pressure transducers
Practical Considerations
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Umbilical with local A/D Belt-pack data logger
SD card Belt-pack wireless
Bluetooth, longer battery life Network wireless
Dropouts, battery life
Data Transfer
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Xsens MVN Biosyn FAB NexGen Ergonomics Microstrain wireless MEMSense Sparkfun WiTilt Nintendo WiiMote
Commercial Systems