from Inertial Body Sensor Networks for

yakzephyrAI and Robotics

Nov 24, 2013 (3 years and 6 months ago)

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Shanshan

Chen, Christopher L. Cunningham
, John
Lach

UVA Center for Wireless
Health

University of Virginia

BSN, 2011


Extracting
Spatio
-
Temporal Information
from Inertial Body Sensor Networks for
Gait Speed Estimation

1

Bradford

C.
Bennett,

Research Statement

2


Signal processing challenge to obtain accurate spatial
information from inertial BSNs


Gait speed as an example to extract accurate
spatio
-
temporal
information


Gait speed is the No. 1 predictor in frailty assessment


require
high gait speed accuracy


d
esire
for continuous, longitudinal gait speed monitoring

Prevailing Technology

--
for Gait Speed Estimation


Nike+®

Pedometer, cadence






3


Fit
-
Bit®:

Accelerometer
, cadence



Garmin
Forerunner
®301

Wearable wrist
GPS,
v
elocity




Stopwatch

and Tape

Inertial BSN for Gait Speed Estimation


Portable Solution for Gait Related Analysis


Also provide other
spatio
-
temporal parameters


Challenges


Spatial Information


Integration
Drift


Mounting Uncertainty


Minimizing Invasiveness


How?


Calculating Stride L
ength/Gait Cycle


Distance =

Stride

Length

of

Gait

Cycle

i
𝑖
=

𝑁
.


𝐶𝑐 𝑠
𝑖
=
1


Average Gait Speed = Distance / Travelling time



4

TEMPO 3.1 inertial BSN platform

developed at the University of Virginia

Contributions

5


Refined human gait model by leveraging biomechanics
knowledge


Improve accuracy without increasing signal processing
complexity


Mounting calibration procedure to correct mounting error


Practical in experiments


Improved gait speed estimation accuracy by combining the
two methods

Outline

6


Current Gait Speed Estimation Method


Gait Cycle Extraction and Integration Drift Cancelation


Stride
Length
Computation by Reference
M
odel


Refined Human Gait Model


Mounting
Calibration


Experiment & Results

Gait Cycle & Integration Drift Cancelation

7


Gyroscope signals on the
sagittal plane


Use foot on ground to find
gait cycle boundaries


Numerically easy to pick
up


local maximum


Helpful for canceling
integration drift


Shank
angle is near zero and
does not contribute to the
stride length calculation
when foot is on
ground


Assume
l
inear drift



Stride Length Computation

𝐿
𝐿 
:
𝐿𝑔

𝐿𝑛𝑔𝑡ℎ



1

= sin(
θ
2
)

×
𝐿
𝐿 



2

=
sin(
θ
2
)
×

𝐿
𝐿 

Stride Length
=



1 
+

2𝐿

+

1𝐿
+

2 


8

Reference Model

S. Miyazaki, “Long
-
Term Unrestrained Measurement of Stride Length

and
Walking Velocity Utilizing a Piezoelectric Gyroscope”


Outline

9


Current Gait Speed Estimation Method


Gait Cycle Extraction & Integration Drift Cancelation


Stride
Length
Computation by Reference
M
odel


Refined Human Gait Model


Mounting
Calibration


Experiments and Results

Inspection of Gait
P
hase

10

11

𝐿
𝐿 
:
𝐿𝑔

𝐿𝑛𝑔𝑡ℎ
,
𝐿
ℎ𝑎
: Shank Length



1

= sin(
θ
𝑖

)

×
𝐿
ℎ𝑎




2

=
sin(
θ
𝑎
)
×

𝐿
𝐿 

Stride Length =

1 
+

2𝐿
+

1𝐿
+

2 

Refined Compound Model

12

Reference Model

Outline

13


Current Gait Speed Estimation Method


Gait Cycle Extraction and Integration Drift Cancelation


Stride
Length
Computation by Reference
M
odel


Refined Human Gait Model


Mounting
Calibration


Experiment & Results

Mounting Calibration

14


Nodes could be rotated 20
°
~30
°

from ideal orientation


Attenuate the signal of interest on the sensitive axis


Essence of Mounting Calibration


Mapping inertial frame (
𝐚
) to global frame(
𝐚
𝐁
) :
𝑹
𝑩𝑰

𝑹
𝑩𝑰
=

𝑩

𝑩

𝑩


Finding

𝑩
,

𝑩
,

𝑩


--

the x, y, z axis (global frame)
represented by


the inertial frame


Accelerometer readings are the orthogonal bases of
the inertial frame





Ideal Mounting

Non
-
ideal Mounting

Mounting Calibration Methods

15


Standing straight to get vector


Lift leg and hold still to obtain the rotated


Assumption: rotating only on the sagittal plane, i.e. only y
-
axis
of accelerometer is rotated, z
-
axis remain perpendicular to
sagittal plane



Cross product to obtain the third vector




Apply calibration





Validation of Mounting Calibration Algorithm

16

Mounting
Position Rotated
Around Y
-
axis

Measured by
Proposed Algorithm

Measurement
Error of Angle

0
°

-
0.072
°

0.072
°

15
°

16.286
°

1.286
°

30
°

27.896
°

2.104
°

45
°

43.954
°

1.046
°

60
°

58.078
°

1.922
°

75
°

74.737
°

0.263
°

90
°

90.461
°

0.461
°

0
0.5
1
1.5
2
2.5

15°
30°
45°
60°
75°
90°
Measurement Error of Angle

Measurement Error of Angle

Pendulum Model to simulate
node rotation on shank


Rotate around z
-
axis with
controlled degree


Determine the rotation by
Mounting Calibration Algorithm


Achieve an average error of ~1
°

Outline

17


Current Gait Speed Estimation Method


Gait Cycle Extraction and Integration Drift Cancelation


Stride
Length
Computation by reference model


Refined Human Gait Model


Mounting
Calibration


Experiment & Results

Treadmill Control of Speed


Is gait on treadmill
different from on
ground?


Gyroscope signals
collected on
treadmill show no
significant
difference from
those collected on
ground

18

Experiments on Treadmill


Two subjects, a taller male subject and a shorter female subject


Two
trials were conducted for each subject, one with well
-
mounted nodes and
another with poorly
-
mounted
nodes to validate mounting calibration


Speeds ranging from 1 to 3 MPH with a 0.2 MPH (0.1m/s) increment for 45
seconds
at each speed



19

Subject with poorly mounted

Inertial BSN nodes performing

mounting calibration on treadmill

Results

Before/After Mounting Calibration

21


Badly mounted nodes causes underestimation of gait speed


attenuation of
signal due to bad mounting


Mounting Calibration has correct the significant estimation error


Before Mounting Calibration

After Mounting Calibration

Results of Two
S
ubjects

22


Significantly reduced RMSE compared to the reference model


Overestimate
at lower speeds and underestimate at higher
speeds


Overestimate
taller
subject’s speeds more than the
shorter subject


Gait Model at Different
S
peeds


The thigh angle can be critical for controlling the step length


23


1



2



1
𝐿


2𝐿


Use thigh nodes to increase accuracy if invasiveness is not a
concern


How accurate is accurate enough?


Depends on application requirement

High Speed


Elimination of thigh angle
results in underestimation of
stride length at high speed


Vice versa at low speed


Results of Two Approaches

24

Double Pendulum at Initial Swing

Single Pendulum Model at Toe
-
off


Better than the reference model


Still overestimate the gait speed



Single Pendulum at Toe
-
Off

Future Work

25


Need more subjects, more gait types, and more gait speeds


For certain types of pathological gait, include those with
shuffling, a wide base, and out
-
of
-
plane motion


More refined gait models will be developed based on
biomechanical knowledge


Evaluate if a training set of data can be used to calibrate the
algorithm for each individual subject

Conclusion

26


Achieving an RMSE of 0.09m/s accuracy with a resolution of
0.1m/s


Proposed model shows significant improvement in accuracy
compared to the reference model


M
ounting calibration corrected the estimation error


Leveraging biomechanical domain knowledge simplifies
signal processing



Thanks!

Q&A