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Wireless Health 2014 Conference Technical Session 3 featuring speaker Jian Wu.
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WLSACONVERGENCE SUMMIT
ZERO-EFFORT CAMERA-ASSISTED CALIBRATION TECHNIQUES FOR WEARABLE MOTION SENSORS
JIAN WUUNIVERSITY OF TEXAS, DALLAS
Zero-Effort Camera-Assisted Calibration Techniques for Wearable Motion
Sensors
Jian Wu and Roozbeh Jafari
Embedded Signal Processing LaboratoryDepartment of Electrical Engineering
University of Texas at Dallas
Motivation & Background
• Wearable motion sensor Inertial measurement unit (IMU) 3-axis accelerometer measures gravity and accelerations and 3-axis
gyroscope measures angular velocities• Where are they used?
Navigation systems Health and wellness monitoring Activity tracking
• Forms Mobile phones Wearable devices Fitness trackers
3
Motivation & Background
• Activity recognition plays an important role in pervasive wellness and health-care monitoring applications.
• The activity recognition algorithms are often designed to work with a known orientation of sensors on the body.
4
0 100 200 300 400 500-2
-1
0
1
2
Sample number
Ac
ce
lera
tio
n(g
)
x-axisy-axisz-axis
0 100 200 300 400 500-1.5
-1
-0.5
0
0.5
Sample number
Ac
ce
lera
tio
n(g
)
x-axisy-axisz-axis
Sit-to-stand
Sit-to-stand
X
Y
X
Y
Motivation & Background
• The orientation may be different from which when the system was calibrated and trained. Accidental displacement during motion The user may not wear the sensor properly
• Calibration of the sensor orientation is necessary. Current approaches: Investigate the statistical distribution of the features, and
adjust the features adaptively for slight displacement. Ask the user to perform a certain activity which will
require extra efforts.
5
Motivation & Background
• We propose a calibration method by leveraging the camera information (i.e., Kinect) which requires zero effort from the user.
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Kinect
Inertial Sensor
Arbitrary movement
Frames Definition
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Human body frame (back view)
Xe
Xs
Ys
Zs
Ye
Ze
gravity
Kinect frame
Sensor local frameSensor earth frameSensor front face
Problem Formulation
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• Yaw rotation: sensor rotational displacement around Y-axis of human body frame
• Roll rotation: sensor rotational displacement along the Z-axis of the human body frame
• Objective: calibration of the sensor yaw rotation (case 1) and roll rotation (case 2) w.r.t. the human body frame
Angle symbol Angle representationβ Yaw rotation of the Kinect frame w.r.t. the
sensor local frameγ Roll rotation of the sensor local frame w.r.t. the
Kinect frameα Yaw rotation of the Kinect frame w.r.t. the
human body frameφ Yaw rotation of the sensor local frame w.r.t.
the human body frame
Proposed Approach
• For a body segment rotation, inertial sensor and Kinect measure the same rotation, and thus has the minimum rotation distance.
• First step yaw search, the sensor yaw rotation w.r.t Kinect frame is calibrated.
• Second step roll search, the sensor roll rotation w.r.t human body frame is calibrated
• In the last step, the sensor yaw w.r.t human body frame is calibrated.
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Algorithm Preliminaries
• Motion sensor: Orientation of the sensor local frame w.r.t. the sensor earth frame, which is denoted as .
• Kinect sensor: The orientation of body segment w.r.t Kinect frame.
• Rotation Distance:
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First Step Yaw Search
• First step search for yaw rotation β The tilt of Kinect is zero The user faces the Kinect
• β degrees yaw rotation between sensor earth frame and Kinect frame.
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First Step Yaw Search
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• For a body segment rotation Sensor measurement:
Kinect measurement:
Minimum rotation distance between and . min(d(, )
First Step Yaw Search
• Movement quality metric µ µ =
: rotation of body segment w.r.t gravity vector. 1 and 2 are two states during one movement. Chosen as 0.1, which is a reliable measure since it
works correctly for all our experiments.
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First Step Yaw Search
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Algorithm 1 First step yaw direction search Calculate; if µ < 0.1 The movement is not qualified, choose another one; else Continue; end for = 1:360 Calculate d(, ; ; end return .
Second Step Roll Search
• Two states: arbitrary and ideal• Rotation of the segment
Kinect measurement
Sensor measurement• =
Rotation distance•
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Algorithm 2 Second step roll rotation search
for = 1:360 Calculate ;(); end return .
Sensor Yaw w.r.t the Human Body
• Through the first step search, the yaw of sensor body frame w.r.t the Kinect frame is calculated as -.
• Yaw rotation between body frame and Kinect frame obtained from Kinect API, denoted as α.
• Sensor yaw rotation w.r.t. the human body frame:
φ = -α
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Experiment Setup
• Four yaw configurations• Two random roll rotations• 4 subjects
(3 male & 1 female)
• Activities
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No# Activity No# Activity1 Walking 4 Sit-to-stand
2 Kneeling 5 Stand-to-sit
3 Leg lifting 6 Arm stretch
Yaw Search Results
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1 2 3 4 5 6 7 8-50
0
50
100
150
200
250
300Yaw search for upper arm
Samples
An
gle
(d
eg
rees)
m1-0deg
m6-0deg
m1-90deg
m6-90deg
m1-180deg
m6-180deg
m1-270deg
m6-270deg
0 5 10 15 20 25 30 35 40-100
0
100
200
300
400Yaw search results for thigh
Samples
Ang
le (d
egre
es)
0 degree yaw90 degree yaw180 degree yaw270 degree yaw
y = 270
y = 180
y = 90
y = 0
Roll Search Results
• Roll search errors for different subjects for arm and thigh
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Subject #
Arm Accuracy(RMSE in degrees)
Thigh Accuracy(RMSE in degrees)
Subject 1 10.733 5.98Subject 2 5.11 5.60Subject 3 13.5 5.32Subject 4 9.60 5.60
Total 10.73 5.59
Activity Recognition Accuracy
Method Activity #
1 2 3 4 5 6
Approach in [5] 93% 98% 92% 100% 93% 100%
Our approach 92% 98% 90% 100% 92% 100%
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Performance: our approach offers similar performance as [5] for the activity recognition applications.
[5] A. Henpraserttae, S. Thiemjarus, and S. Marukatat, “Accurate activity recognition using a mobile phone regardless of device orientation and location,” in Body Sensor Networks (BSN), 2011 International Conference on, pp. 41–46, IEEE, 2011.
Conclusions
• Wearable motion sensors need to be calibrated for activity recognition applications.
• We proposed a zero-effort camera-assisted calibration method.
• Our results show good performance of the yaw calibration and an average RMSE of 10.73 degrees for arm and 5.59 degrees for leg for the roll calibration.
• Our method achieves similar performance as classic calibration techniques that require extra efforts.
21
Questions
Zero-Effort Camera-Assisted Calibration Techniques for Wearable Motion Sensors
Jian [email protected] Signal Processing Lab, UT-Dallashttp://www.essp.utdallas.edu
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WLSACONVERGENCE SUMMIT
www.wirelesshealth2014.org