1
Active Tremor Compensation via Robotic Handheld Instrument and Wearable Orthosis
Wei Tech ANGAssistant Professor
School of Mechanical and Aerospace EngineeringNanyang Technological University, Singapore
2
Active Noise/Disturbance Compensation
Low signal-to-noise ratio applicationsPhysiological tremor – micromanipulation Pathological tremor – activities of daily living
External Disturbance
System
Intended Motion
Filtering
Disturbance + Intended motion
Motion Sensing
ControllerDisturbance
Manipulator
Disturbance Compensation
One sampling cycle
3
Active Pathological Tremor Compensation in Wearable Orthosis
Nanyang Technological UniversityDingguo ZHANG Nanyang Technological UniversityFerdinan WIDJAJANanyang Technological UniversityKalyana VELUVOLU Tan Tock Seng HospitalAdela TOWNational Neuroscience InstituteLouis TANNanyang Technological UniversityCheng Yap SHEE Technische Universitaet MuenchenMarkus RANK (Exch Student)LIRMM, U of Montpellier IIPhilippe POIGNETNational Neuroscience InstituteWing Lok AU
4
Active Pathological Tremor Compensation in Wearable Orthosis
5
Active Physiological Tremor Compensation in Handheld Microsurgical Instrument
Nanyang Technological UniversityMingli HAN
Nanyang Technological UniversityTun Latt WIN Nanyang Technological UniversityKalyana VELUVOLU Nanyang Technological UniversityU-Xuan TAN Nanyang Technological UniversityCheng Yap SHEE Carnegie Mellon UniversityCameron RIVIERE Singapore General HospitalYee Siang ONG National University HospitalThiam Chye LIM
Carnegie Mellon UniversityDavid CHOI
6
Microsurgery with Active Handheld Instrument
Visuomotor Control System
Noisy, Tremulous Motion
Motion Sensing
Visual Feedback
Micron
Microsurgery
Estimation of erroneous motion
Tip manipulation for active error compensation
Task Definition
Problem Analysis
Engineering Solutions
Technical Details
7
Active Physiological Tremor Compensation in Handheld Microsurgical Instrument
8
Involuntary Hand Movement for Healthy People
Physiological Tremor
Roughly sinusoidal, ≤50 µm rms, 8-12 Hz
Others: Myoclonicjerk, drift
9
Physiological Tremor and Microsurgery
Complicates microsurgical procedures and makes certain delicate interventions impossible
10
Vitreoretinal Microsurgery
Removal of membranes ≤ 20 µm thick from front or back of retina
11
Vitreoretinal Microsurgery
Injection of anticoagulant using intraocular cannulation to treat retinal vein (~∅100 µm) occlusion
12
Physiological Tremor and Microsurgery
13
Physiological Tremor and Microsurgery
Impact on microsurgeons2 of 10 surgeons become microsurgeons
Factors affecting tremorFatigue – strenuous exercise etc.Caffeine/alcohol consumption (withdrawal syndrome)Lack of practice – long vacation etc.Age – experience vs hand stability
Microsurgeons’ consensus:10 µm positioning accuracy
14
Microsurgery with Active Handheld Instrument
Visuomotor Control System
Noisy, Tremulous Motion
Motion Sensing
Visual Feedback
Micron
VitreoretinalMicrosurgery
Estimation of erroneous motion
Manipulation of tip for active error compensation
15
Comparison of Robotic Solutions
Telerobotics‘Steady Hand’ robot Active Handheld Instrument
CheapUnobtrusiveDexterityLimited workspaceNo motion scalingNo ‘third hand’
Obtrusive
Unobtrusive< US$15K
> US$150K> US$1M
Da Vinci Steady Hand robot, Johns Hopkins Univ,
16
Micron Current Prototype
Length: 150 mm long Diameter: Ø20(16) mmWeight <100 g6 DOF inertial at the back end3 DOF piezoelectric driven parallel manipulator at front end with disposable surgical needleSignal processing and control performed by PC via ADC & DAC
150 mm (w/o needle)
Ø20 mm
Ø16 mm
Manipulator System
Sensing System
Disposable surgical needle
17
Microsurgery with Active Handheld Instrument
Visuomotor Control System
Noisy, Tremulous Motion
Motion Sensing
Visual Feedback
Micron
VitreoretinalMicrosurgery
Estimation of erroneous motion
Manipulation of tip for active error compensation
• Resolution
• Accuracy
18
On-board Sensing System
All-accelerometer inertial measurement unit (IMU):
3 dual-axis miniature MEMS accelerometers Analog Devices ADXL-203: 5mm x 5mm x 2mm, < 1g
Housed in 2 locations
Back Sensor Suite
Sensing direction
Front Sensor Suite
ZB
XB
YB
Dual-axis accelerometer
Dual-axis accelerometers
Manipulator System
19
Differential Sensing KinematicsBody acceleration sensed by accelerometer at location i:
Differential Sensing
Solve system of nonlinear equations for Ω = [ωx ωy ωz]T by Gauss-Newton or Levenberg-Marquart method
onsAccelerati inducedRotation−
×+×Ω×Ω++= BiBiCGi PPgAA α
Xw
P23
P13
P12
P3
P2P1
Yw
Zw
W
B 2
3
1
Y2
Z2
( )
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡••
=⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
•
•=
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
••=
=×+×Ω×Ω=−=
z
y
x
ijijij
aAaA
aA
jiPAAA
13
122323
13
13 ,,
3 2, 1,,,][]][[ α
20
Sensing Kinematics
Updating quaternions:
Directional Cosines matrix
Gravity Removal: WAE = WCBBA – Wg
Tip Displacement:
⎥⎦
⎤⎢⎣
⎡Ω−
Ω×Ω=ΩΩ=
×
××
0][
21~ ),()(~)(
31
1333Ttqttq
⎥⎥⎥
⎦
⎤
⎢⎢⎢
⎣
⎡
+−−+−−−+−++−−−+
=23
22
21
2010322031
103223
22
21
203021
2031302123
22
21
20
)(2)(2)(2)(2)(2)(2
qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq
CBW
tipBB
BW
t
TtE
Wtip
Wtip
W PtCddATtPtP ])[()()()( ×Ω++−= ∫ ∫−
τττ
21
Sensing Resolution (Error Variance) Analysis
Sensing resolution dependent on sensor noise floorAngular Sensing
Sensing equation:Aij = f(Ω)= ([Ω×] [Ω×] + [α×])Pij
Covariance:C(Aij) = C(Ω) Pij
Pij↑, C(Ω)↓
Pij
σAx2
σAy2
σAy2
σAx2
σωAx2 orσωAy
2
Back sensor suite
Front sensor suite
22
Proposed All-Accelerometer vs Conventional Inertial Measurement Unit
All-accelerometer IMUMaximized Pij, with physical constraint of a slender handheld instrument
Conventional IMU (3A-3G)Tokin CG-L43D rate gyros x 3
96.9% / 32x4.42 × 10-21.41ωz
99.3% / 130x1.08 × 10-21.41ωx & ωy
Noise reduction / resolution
improvement
6A Error std. dev.
(deg/s)
3G-3AError std. dev.
(deg/s)
23
Angular Sensing Resolution Comparison
Small angular velocity & sensor noise floor
0 50 100 150 200 250 300 350 400 450 500-2
0
2
4
6
8
0 50 100 150 200 250 300 350 400 450 500-2
0
2
4
6
8
0 50 100 150 200 250 300 350 400 450 500-2
0
2
4
6
8
ωz
(deg
/s)
ωx, ω
y(d
eg/s
)
ωG
(deg
/s)
Time (ms)Time (ms)
Tokin Gyroscope
All-accelerometer IMU
24
Sensing Resolution (Error Variance) Analysis
Translational Sensing2 accelerometers in each sensing direction:
Sensing resolution improves by a factor of
Better orientation estimation → more complete removal of gravity → better translation estimation
2111222
AiA
AjAiA
σ=σ→
σ+
σ=
σ2
25
Integration Drift of Inertial Sensors
Integration driftErroneous DC Offset
Ramp QuadraticError accumulates and grows unbounded over time
Poor sensing accuracy
∫∫0 0.2 0.4 0.6 0.8 1
-1
-0.5
0
0.5
1Acc
eler
atio
n (m
m/s
2 )
0 0.2 0.4 0.6 0.8 10
0.02
0.04
0.06
Vel
ocity
(mm
/s)
0 0.2 0.4 0.6 0.8 1
0
0.01
0.02
0.03
Time (s)D
ispl
acem
ent (
mm
)
Mean = 0.05 mm/s2
26
Measurement Model
Measurement model allows error analysis and compensationMeasurement Model = Physical (Deterministic) Model + Stochastic Model
Measurement Error
Measurement Model• Physical
• Stochastic
Compensation
27
Experimental Observations
-1 -0.5 0 0.5 11.5
2
2.5
3
3.5
Acceleration (g)
Acc
eler
omet
er O
utpu
t (V
)
A
B+
+
−
−
CW: A+ → B-
CCW: B+ → A-
g
-1 -0.5 0 0.5 11.5
2
2.5
3
3.5
Acceleration (g)
Acc
eler
omet
er O
utpu
t (V
)
α±150
α±90
g
α = 90°, β = -180° to 180°
α±30
α = 30° & 150°, β = -180° to 180°
28
Phenomenological ModelingBias, Bx(Vy, Vz) = Bx+gx(Vy)+hx(Vz)Scale Factor,SFx(Vz) = rx2Vz
2 + rx1Vz + rx0
Model
Ax = (Vx− Bx(Vy, Vz)) / SFx(Vz)
-1 -0.5 0 0.5 11.06
1.07
1.08
1.09
1.1
1.11
z-Acceleration (g)
Sca
le F
acto
r (V
/g)
1.5 2 2.5 3 3.5 4-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
y-Accelerometer Output, Vy (V)
Res
idue
Rxy (V
)
-1 -0.5 0 0.5 1-5
0
5
10
15x 10-3
z-Acceleration (g)
Res
idue
Rxz (V
)
gx(Vy) = px2Vy2 + px1Vy + px0
hx(Vz) = qx2Vz2 + qx1Vz + qx0SFx(Vz) = rx2Vz
2 + rx1Vz + rx0
In plane cross- axis effect
Out of plane cross- axis effectScale Factor
290 0.2 0.4 0.6 0.8 1-500
-400
-300
-200
-100
0
100
200
Time (s)
Acc
eler
atio
n (m
m/s
2 )Sensing Results - Translation
0 0.2 0.4 0.6 0.8 1-150
-100
-50
0
50
100
150
200
Time (s)
Acc
eler
atio
n (m
m/s
2 )
--89.7Error Reduction (%)
<1<531*Proposed Physical Model
6272300Linear Model
Scale Factor (mm/s2)Bias (mm/s2)Rmse (mm/s2)
Linear Model Proposed Physical Model
* ADXL-203 rated rms noise = 22.1 mm/s2
Interferometer
Accelerometer
30
Residual Zero Offset - Integration Drift
0 0.2 0.4 0.6 0.8 1
-0.1
-0.05
0
0.05
0.1
0.15
Time (Sec)
0 0.2 0.4 0.6 0.8 1-2
0
2
4
6
8
10
12
14x 10-3
Time (Sec)
0 0.2 0.4 0.6 0.8 10
1
2
3
4
5
6
7x 10-3
Time (Sec)
Acceleration
Position
Velocity
Numerical Integration
Zero Offset
31
Analytical Integration
0( )
0 00
sin(2 ( ) ) cos(2 ( ) )L f f G
k r rr
r ry a f k b f kG G
π π= −
=
= + + +∑
0 02 20 0 0
sin(2 ( ) ) cos(2 ( ) )(2 ( )) (2 ( ))
Lr r
k r rr G G
a br ry f k f kf G f G
π ππ π=
⎡ ⎤= − + + +⎢ ⎥+ +⎣ ⎦
∫ ∫ ∑
Acceleration:
Velocity:
Position:
0 00 0 0
cos(2 ( ) ) sin(2 ( ) )(2 ( )) (2 ( ))
Lr r
k r rr G G
a br ry f k f kf G f G
π ππ π=
⎡ ⎤= − + − +⎢ ⎥+ +⎣ ⎦
∫ ∑
Real-time modeling of Tremor
32
Analytical Integration
0 0.2 0.4 0.6 0.8 1-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
Time (Sec)0 0.2 0.4 0.6 0.8 1
-0.1
-0.05
0
0.05
0.1
0.15
Time (Sec)Acceleration
0 0.2 0.4 0.6 0.8 1-8
-6
-4
-2
0
2
4
6x 10-5
Time (Sec)
Estimation with BMFLC
Analytical Integration
Velocity
Position without drifting
Zero Offset
33
Vision Aided Inertial Sensing
Sensor Fusion (drift
correction)
High speed camera
Microscope
Micron
Inertial Sensors
Pose Estimate
34
Vision Aided Inertial Sensing
Instrument tip
Pan angle
Tilt angle
Gravity sensed by the accelerometers is used to obtain tilt angle of the instrument
Microscope with Camera
8x Magnification:• Tip Position Error = 2.13 µm rms• Tip Pan Angle Error= 0.2° rms.
35
Sensor Fusion
Bias
+
_Scale Factor
Local Gravity
Misalign-ment
Correction
Location & Orientation
Error
Augmented State Kalman
Filtering
Acc. Trend
Acceleration Random
Walk1/s
Velocity Random Walk
+
+
Acc.
Cam.
Noise removal
Physical Model
Edge Detection
Hough Transform
Tilt
KalmanFiltering
(Quaternion)
Kinematics
Tool tip displacement
Analytical Integration
Dilation +
Erosion
MotionDispl.
Pan
Displ.
36
Microsurgery with Active Handheld Instrument
Visuomotor Control System
Noisy, Tremulous Motion
Motion Sensing
Visual Feedback
MICRON
VitreoretinalMicrosurgery
Estimation of erroneous motion
Manipulation of tip for active error compensation
• Real-time
• Phase lag
37
Zero Phase Filtering
Phase lag = time delaySeparation of tremor from the intended motion without introducing phase lag
Prediction/projection capabilityAdaptive
Non-linear phase response of IIR filter, i.e. phase characteristic changes with frequency
38
Fourier Linear Combiner (FLC)
Truncated Fourier series to adaptively estimate amplitude and phase of periodic signal with known frequency (ω0)
39
Weighted-frequency Fourier Linear Combiner (WFLC)
Extends FLC to also adaptively estimate the frequency using another LMS algorithmBand-pass filter to select the band of interest
Assumption: rate of change of the dominant input signal frequency is slow
Zero-phase notch (band-stop) filter effect
FLC
WFLC
Signal input
ω0
Tremor
BandpassFilter
40
Weighted-frequency Fourier Linear Combiner (WFLC)
41
Weighted-frequency Fourier Linear Combiner (WFLC) Experiment
1 DOF motion canceling experimentAve. rms tremor amplitude reduced 69%
42
Tremor Recordings
0 5 10 15 20 25 30 35 400
0.5
1x 10-5 Spectrum of the Signal
0 5 10 15 20 25 30 35 400
0.5
1 x 10-5
0 5 10 15 20 25 30 35 400
0.5
1 x 10-5
Frequency
Band of frequencies
Band of frequencies
Band of frequencies
43
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 55
6
7
8
9
10
11
12
Time (sec)
Fre
quen
cy (
Hz)
Frequency adaptation in WFLC
The frequency adaptation becomes unstable due to the presence of two frequencies 8 & 8.6 Hz
44
Bandlimited Multiple FLC
To estimate the tremor signal within a band of frequencies or comprising of multiple frequency components (modulated signals)
Corresponding weight will adapt to the corresponding frequency of the input signalCan deal with input signals of multiple frequency components unlike WFLC
45
0 1 2 3 4 5−3
−2
−1
0
1
2
3
4
Time (sec)
WFLC Vs. BMFLC
BMFLCWFLC
Comparative Performance : Estimation Errors
Presence of two frequencies 8 and 8.6 degrades the performance of WFLC
46
Performance of BMFLC with Real-Tremor
0 5 10 15 20 25-1
0
1x 10-4 Tremor signal and compensated signal in x-axis
0 5 10 15 20 25-1
0
1x 10-4 Tremor signal and compensated signal in y-axis
0 5 10 15 20 25-1
0
1x 10-4 Tremor signal and compensated signal in z-axis
Red line shows the compensated motionBand of BMFLC: 3- 12 Hz.
47
0 5 10 15 20 25 30 35 400
0.5
1x 10-5 Spectrum of the Signal
0 5 10 15 20 25 30 35 400
0.5
1x 10-5
0 5 10 15 20 25 30 35 400
0.5
1x 10-5
Frequency
Spectrum of the compensated motion.
Performance of BMFLC with Real-Tremor
48
Analytical Integration via BMFLC
0( )
0 00
sin(2 ( ) ) cos(2 ( ) )L f f G
k r rr
r ry a f k b f kG G
π π= −
=
= + + +∑
0 02 20 0 0
sin(2 ( ) ) cos(2 ( ) )(2 ( )) (2 ( ))
Lr r
k r rr G G
a br ry f k f kf G f G
π ππ π=
⎡ ⎤= − + + +⎢ ⎥+ +⎣ ⎦
∫ ∫ ∑
Acceleration:
Velocity:
Position:
0 00 0 0
cos(2 ( ) ) sin(2 ( ) )(2 ( )) (2 ( ))
Lr r
k r rr G G
a br ry f k f kf G f G
π ππ π=
⎡ ⎤= − + − +⎢ ⎥+ +⎣ ⎦
∫ ∑
• Frequency components remain constant in BMFLC
• Once the weights adapt, the weights can also be assumed to be constant
By Performing analytical integration:
49
Microsurgery with Active Handheld Instrument
Visuomotor Control System
Noisy, Tremulous Motion
Motion Sensing
Visual Feedback
MICRON
VitreoretinalMicrosurgery
Estimation of erroneous motion
Manipulation of tip for active error compensation
•High precision tracking control
• Mechanism design
50
Manipulator Design
3 DOF piezoelectric-driven parallel manipulator
1 actuator per axis, max effective stroke = 12.5 µmMotion amplification = 9.4x, total stroke > 100 µm
Tool tip approximated as a point, hence only 3 DOF manipulationParallel manipulator design because
Rigidity, compactness, and design simplicity
51
Design of Parallel Mechanism
Flexure Based MechanismMonolith design using Stereolithography(SLA)∅22 x 58 mmIEEE EMBS 2005 (Shanghai) Best Student Design Competition winner
David Choi (CMU) et al.
52
25 Hz
15 Hz
5 Hz
Piezoelectric Actuator Hysteresis
Pros : High bandwidthFast responseHigh output force
Cons : Hysteresis
~15% of max. displacementHysteresis is rate-dependent
0 20 40 60 80 1000
2
4
6
8
10
12
14
Voltage (V)
Dis
plac
emen
t (µm
)
53
0 20 40 60 80 10010
20
30
40
50
60
70
80
90
0 0.2 0.4 0.6 0.8 1-20
0
20
40
60
80
100
Commercial Piezo-System with Feedback Controller
Piezo-driven 3 axis micro-positionerPolytec-PI, Germany, NanoCubeTM P-611>$ 10,000Feedback sensors: strain gagesTracking a 10 Hz, 100 µm p-p sinusiod
Hysteresis still presentLow-pass filtered behavior
Time (s)
Dis
plac
emen
t (µm
)
Measured Desired
Dis
plac
emen
t (µm
)
Voltage (V)
54
Feedforward Controller with Inverse Hysteresis Model
Develop an invertible mathematical model that closely describes the hysteretic behavior of a piezoelectric actuatorPrandtl-Ishlinskii Model
0 2 4 6 8 10 12 140
20
40
60
80
100
0 20 40 60 80 1000
2
4
6
8
10
12
14
0 2 4 6 8 10 12 140
2
4
6
8
10
12
14
Feedforward controller Piezoelectric
Desired Displacement (µm)
Actuator Response (µm)
Voltage(V)
Displacement (µm)
Dis
plac
emen
t (µm
)
Voltage (V)
Vol
tage
(V)
Actuator Response (µm)
Displacement (µm)
Dis
plac
emen
t (µm
)
Desired Displacement
(µm)
55
Prandtl-Ishlinskii (PI) Operator
Rate independent backlash operator:Hr = maxx(t) – r, minx(t) + r, y0Linearly weighted superposition of backlash operators:
r1
-r1
y
x
wh1
ri
y
x
Whi = Σwhi
[ ] )(,)( 0 tyxHwty rTh=
56
Inverse PI Hysteresis Model
Hysteresis Model
Inverse Model
Reflection about Y = X (45° line)
57
Tremor Tracking Results
Tracking recordings of real tremor using 1 piezoelectric stackRmse = 0.64% of max ampl.; Max error = 2.4% of max ampl.
0 0.2 0.4 0.6 0.8 2-2
0
2
4
6
8
10
12
14DesiredObtainedError
0 0.2 0.4 0.6 0.8 1-2
0
2
4
6
8
10
12
14DesiredObtainedError
Without Model Rate-Dependent Controller
Time (s) Time (s)
Dis
plac
emen
t (µm
)
Dis
plac
emen
t (µm
)
58
Adaptive Feedforward Controller
Eliminate parameter identificationWeight adapting mechanism: Recursive Least Square
[ ][ ] )(,)( 0 tyxHzwSw rThd
Ts p
)())(( tzbatzw iihi +=)(tzThw
Thw'
59
Experimental Results
1.30max error / actuator’s stroke length (%)
0.3899 ± 0.0291max error ± σ (µm)
0.31rmse / actuator’s stroke length (%)
0.0943 ± 0.0159rmse ± σ (µm)
Adaptive Rate-dependent Controller
0 5 10 15 200
5
10
15
20
25
30
Time(s)
Dis
plac
emen
t(um
)
measureddesirederror
5 5.5 6 6.5 7 7.5 8
0
5
10
15
20
25
30
Time(s)
Dis
plac
emen
t(um
)
measureddesirederror
60
Real-time Active Compensation – 1 DOF Disturbance, 1 DOF Compensation
61
Real-time Active Compensation – 1 DOF Disturbance, 3 DOF Compensation
62
Real-time Active Compensation –Handheld
C. Riviere (2006), Carnegie Mellon Univ.5 DOF sensing by 2 orthogonal position sensitive detectors No inertial sensingNon-surgical scenario
63
Active Pathological Tremor Compensation in Wearable Orthosis
64
Pathological Tremor
65
Pathological Tremor: Causes & Symptoms
3-12 HzFrom < 10 mm (fingers) to > 100 mm (arm)Common medical conditions
Essential tremor (postural tremor)Parkinson’s disease (resting tremor)Cerebellar dysfunction (intention tremor)
e.g. stroke, multiple sclerosis,Wilson’s disease
66
Impact of Pathological Tremor
buttoning writing Inserting a keytyping
Affects 5-9% of the population age > 40Activities of daily living become challenging or impossible
Social embarrassment and isolationLifetime cost per patient > US$1M
67
Treatment Options
Drug therapy> 50% are not responsive to drug
Stereotactic surgeryCost, psychological barrier, chances of complication
Assistive technologyActive tremor compensation via wearable orthosis
A 20-100 ms electromechanical time delay between Electromyograph (EMG) signals and muscle actuations
68
Active Tremor Compensation in Wearable Orthosis
69
Tremor Filter EMG Filter
Musculo-skeletal system
SensorFusion
Parameters Identification
Tremormodel
SignalProcessing
Inv. ArmModel
FES-Controller
Tremor EMG
Intended EMG
EMG
Acc
Tremor motion
Intended motion / position info
Model information
Camera systemSystem Overview
Modeling
Sensing
Filtering
Compensation70
Feedback Control System
Manipulator
Musculoskeletal System
AmplifiersStimulatorsController
Sensors
e.g. encoder
Sensors
e.g. EMG
71
Electrical Motor Muscle
Robot Arm vs Human Arm- Actuation system
(Hill-Type)
72
Robot Arm vs Human Arm- Links
VS.
Kinematics & Dynamics are solved in similar ways
Manipulator
Skeleton
73
Musculoskeletal Model of Upper Limb
Well studied and established
74
Key Challenges
SensingHow can we know what upper limb movement (tremulous + voluntary) will occur from the sensed SEMG of the muscles?
?
75
Key Challenges
FilteringHow can we differentiate between tremulous & voluntary SEMG of the muscles?
Tremor
Intended Movement
76
Key Challenges
Functional Electrical StimulationHow can we use FES to generate (anti-)tremulous movement in the upper limbs?
Stimulator
Controller
77
Musculoskeletal Modeling of Tremor in Upper Limbs
To understand the roles and characteristics of the skeletal muscles responsible for each type of tremorTo study SEMG-movement relationship
78
Tremor Study
120 patients with movement disorderParkinson Diseases – resting tremor (>30)Essential Tremor – postural tremor (>30)Multiple Sclerosis, Stoke, etc. – intention tremor (>30)Others (~10)
30 healthy peopleAge 16-85No personal & family medical history of tremor
National Neuroscience Institute, Singapore
79
Pathological Tremor Study18 control subjects, 5 Parkinson’s Disease patients, 6 Essential Tremor patients, 1 Psychogenic tremor patient, and 1 Holmes’ tremor patient
80
Data Collection
Record sensor data of patients performingStandard diagnostics: finger to noise, finger to finger, stretched out, drawing spirals, etc.
SensorsSEMG: 16 channels (8 muscle groups, mostly agonist & antagonist pairs) per limbAccelerometers: 3 x tri-axial per limbGoniometers: 1 per limbPosition and Orientation sensor: Vicon optical sensing system (4 cameras)
81
Sensing
SEMG
Accelerometers
Orthosis
SEMG
Accelerometers
Goniometer
Optical Sensors
Model
Sensor
Fusion
Muscle Activity & Physical Movement
82
CE : Contractile Element
SE : Series Elastic Element
PE : Passive Element (Parallel Elastic Element)
Muscle mechanical model (Hill-type)
Muscle Contraction Property
83
ϕ
211
2
211
2 ωωω
++ DsK
The spring damper systemis tuned to the actual tremorfrequency
Phenomenological Modeling of Upper Limb Tremor from EMG
84
Sensor Fusion – Kalman Filtering
EMG-derived joint angle as predictorACC-derived joint angle as corrector
Estimate of joint angle
( ) (1) ( ) (2)( ) (1) ( ) (2)
EMG EMG EMG
ACC ACC ACC
k c EMG k ck c ACC k c
θθ
= += +
2
2 2( ) ( ) ( ( ) ( ))EMG
EMG ACC EMGEMG ACC
k k k kσ
θ θ θ θσ σ
= + −+
85
Results
σ2true = 0.1045
σ2error = 0.022 (78.76% reduction)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
time (s)
degr
eeKalman filter for elbow flextion-extension of PD patient (sitting, arm resting on thigh)
estimatedtrueerror
86
Results
0 5 10 15 20 250
0.2
0.4
0.6
0.8
1
X: 5.054Y: 0.902
Frequency content of the compensated tremor
frequency (Hz)0 5 10 15 20 25
0
2
4
6
8
X: 5.029Y: 7.315
Frequency content of original tremor
frequency (Hz)
Power of original tremor = 7.315 Power of compensated tremor = 0.902
The power of the tremor is reduced by 87.67%
87
Target circle
••• NN outputRaw trajectory
∆ Start position End position* Target circle
Tremor Filtering via ANN
Cascade Correlation Neural Networks with extended KalmanFiltering Experiments with 11 multiple sclerosis patients (intention tremor)Smoother trajectoryReach and dwell in target circle 31.8% faster
Mean over 29 tests
Output
Hidden
Input
88
Functional Electrical Stimulations
Controlling electrical pulses to stimulate the intact peripheral nerve to actuate muscles
Usually used to restore the motor functions for the paralyzed patients
Prochazka et al. (1992) demonstrated the effectiveness of FES for tremor attenuation
Offline trial & error tuning of intensity and phase
89
Fatigue
Muscle Activation
90
Stimulation Artifact
Blocking windowTurn off EMG channels when stimulation is on
91
FES Controller Design
92
0 2 4 6 8 10-800
-600
-400
-200
0
200
400
600
time [sec]
angu
lar v
eloc
ity [d
eg/s
ec]
Simulation Result of Tremor Suppression
No ethical clearance on patient trial yet
93
Wearable Orthosis
Wearable bands thin enough to be worn under sleevesBattery poweredMicrocontroller basedBeyond the first step
False alarmEEG
94
Questions & Comments
Wei Tech ANGSchool of Mechanical & Aerospace Engineering
Nanyang Technological UniversitySingapore
[email protected]://www.ntu.edu.sg/mae/centres/rrc/biorobotics/biorobotics.htm