A Body Sensor Network for Tracking
and Monitoring of Functional Arm
Motion
Kim Doang Nguyen I-Ming Chen Zhiqiang Luo
Song Huat Yeo Henry Duh
School of Mechanical and Aerospace Engineering
Nanyang Technology University, Singapore
2009 IEEE/RSJ Int’l Conf on Intelligent Robots and Systems (IROS)
St Louis, MO, USA October 12-14, 2009
Motivation
• Objective measurement of motor recovery in
patients
– Assessment & evaluation of patient’s strength, range
of motion, muscular activation patterns in recovery
– (Subjective) Measures: impairment (Fugl-Meyer
assessment), daily activity (Barthel index)
• Compact, user-friendly measurement
devices
– Accurate, easy to wear, ergonomic, hygienic,
inexpensive
– For clinical and personal uses
– Motivating patients for rehab program/sessions
with suitable interactive applications
Outlines
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•
•
•
•
•
Motivations and background
OLE sensing module
Body sensor network for OLE & SmartSuit
Sensor placement and arm kinematic model
Experimental results and statistical tests
Conclusion
Optical Linear Encoder Sensor – Joint
Flexion Angles
• Principle of joint flexion angle measurement sensor
– Opto-mechanical strip encoder in bend guard (soft exo-skeleton)
– Single axis linear motion (synthesis for multi-dof)
Wire
Encoder
Reflective linear code
strip
Flexible base
structure
Cable/Fiber with linear graduation
Optical Linear Encoder
x  D

D
 2R  
360

300 lpi encoder = 0.1 deg res.
Elbow
(avg R = 50mm)
D
 360
2 R
Prototype of OLE Sensor
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•
•
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Encoder: AEDR-8400-132 (Avago)
Accelerometer: LIS3LV02DQ (STMicroelectronics) (640Hz)
Microcontroller : dsPIC33FJ32MC204 (Microchip)
CAN controller : MCP2515 (Microchip)
Operating voltage: 3.3V
Sampling frequency: 15kHz
Accuracy: 0.1°flexion angle
Operating current: 7 mA
Maximum reader speed: 1.5 m/s
Optical Linear Encoder
&
SmartSuit
Body Sensor Network
SmartSuit sensor network for capturing arm motion:
three sensor nodes and one data concentrator,
connected via CAN bus
RF station
Human Arm Kinematic Model
3 OLE + accelerometer to obtain 7-DOF Arm
Shoulder (3); Elbow (1); Wrist (2)
1
• At shoulder, θ0=θw0. θw1 from
Acc-1
• θ1 calculated coord transf
• At elbow, θ2 from OLE-1;θ3 from
OLE-2
• Expand trans matrix from world to
frame 4 givesθ4
• At wrist,θ5 from OLE-3;θw4 & θw6
from Acc-3
• Expand trans matrix from world to
frame 6 gives θ6
2
3
Smart Suit App Demo
Validation of OLE Sensing Modules
To testify working principle of OLE:
D 360
 .
R 2
To appraise performance with rigid links and joints,
known joint diameter and center.
Jig diameter: 63 mm
PowerCube:
0.180/pulse
Encoder: 2000
counts/rev
Validation of OLE Sensing Modules
• Three sets of measurements
• Good repeatability with correlation coefficient: 0.99
• Linearity: 99.2%
• Joint diameter
computed from data is
62.8 mm, very close
to measured diameter
of 63 mm
Validation of OLE Sensing Modules
On-body tests: wearing OLE on human against
BOPAC’s Goniometer and ShapeWrap from
Measurand.
• Goniometer measures change of resistance in strain gauges
• ShapeWrap measure bend and twist angle of fiber-optic tape via
light difference between inlet and outlet
Validation of OLE Sensing Modules
Close relation of OLE’s performance v.s. Goniometer and ShapeWrap.
• OLE able to handle high frequency excitation, but better with low
frequency excitation
• Average RMS error
• OLE versus Goniometer is 3.8° with average correlation
coefficient of 0.990;
• OLE versus ShapeWrap is 3.1° with average correlation
coefficient of 0.992.
normal flexion of 0.6 Hz
fast flexion of 2Hz
Benchmark with VICON
• Mean of angle difference μ = -1.835˚
• Standard Deviation of Vicon and OLE : σ = 3.332˚
Validation of SmartSuit system
• To examine repeatability and reliability of SmartSuit
• To testify SmartSuite as a complete arm motion capture system
Experimental procedure adapted from a therapy section of stroke rehabilitation
Validation of SmartSuit system
• One data block file contains 10 trials of
arm-reaching task
• 5 data blocks to produce 5 avg readings
for each sensor
• Range and SD for each subject computed
• average range: 2.819°
• average standard deviation: 0.697°
Validation of SmartSuit system
ICC describes relative magnitude of two components of the variability.
 e2
Approximation of ICC in short form: ICC  2
 e   t2
2
 e is variability of random errors
 t2 is variability among their average values computed over each
repeated measure
As
As
 t2
 e2 decreases, measurement error explains a decreasing
percentage of variance in data, reliability increases, and ICC
approaches maximum value of 1.
 t2
 e2 increases, measurement error explains an increasing
percentage of variance in data, reliability decreases, and ICC
approaches minimum value of 0.
Validation of SmartSuit system
ICC analysis performed for each sensor using Statistical Package for
Social Sciences (SPSS)
• Average ICC for each sensor ranged from 0.959 to 0.975
• Overall average: 0.967±0.08
 e2
ICC  2
Average ICC is close to 1.00, indicating high reliability.
 e   t2
High ICC values for all channels showing ability to perform and
maintain its functions in routine circumstances, with different
biometric subjects.
INTRA-CLASS CORRELATION COEFFICIENT OF RELIABILITY
SHOULDER
ELBOW
0.975
0.974
WRIST (BEND) WRIST (ROLL) AVERAGE
0.959
0.962
0.967
Conclusion
• Low cost high performance joint flexion sensor
– Patented optical encoding strip technology
– Accuracy of ± 0.1º and sampling rate of 1000Hz
– Flexible configuration and usages
• SmartSuit (Arm) based on OLE + Accelerometer
• OLE sensor user validation
• SmartSuit user validation
• Clinical test bedding & rehab application development
Thank You for Your Attention !
Team Members:
A/P Yeo Song Huat
A/P Ling Keck Voon
Dr. Peter Luo
Dr. Zhongqiang Ding
Dr. Chee Kian Lim
Dr. Yan Liang
Wei-Ting Yang
John Nguyen
Kang Li
Wei Ni
Chao Gu
Ke Yen Tee
Collaborators:
A/P Henry Duh (NUS)
Prof T-Y Li (NCCU, TW)
Prof M. Ceccarelli (Uni
Cassino, IT)
Prof G. Stepan (BME, HG)
Funding support:
School of MAE, NTU
ASTAR SERC
ASTAR EHS II Program
ASTAR MedTech Program
ASTAR – NKTH
NRF IDM (MDA)