An Ultra-Low-Power Human
Body Motion Sensor Using
Static Electric Field Sensing
CSC2228 Presentation
Omar Wagih
What is human body
motion sensing?
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Device that takes in motion and converts it to an electric
signal
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Signal is processed to understand position and orientation
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Two types of body motion sensing
Wide range of applications (e.g. gaming, computer
controllers, health and wellness, monitoring)
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Wearable sensors
Remote sensors
Wearable sensors
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Examples include:
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Wii Controllers
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MYO armband
Razar Hydra Gaming
controllers
Mostly use accelerometers or
magnetic fields to detect
absolute position and
orientation
Remote body sensors
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Examples include:
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Microsoft Kinect
Leap motion controller
Mostly use computer vision
and or infrared light
Demo
Wearable sensors
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Primarily developed using accelerometers
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Accelerometer consists of a mass, and when the
sensor is moved, the mass moves
3-axis accelerometer which is capable of detecting
acceleration in 3 dimensions (up and down, left
and right, back and forth)
Problem: Power consumption
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Typically require high power to consistently
provide signal
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Power consumption being highly optimized as
technology develops
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Lowest commercial accelerometer uses 400-1000
µW. Research devices consume as little as 36 µW
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But can we do better? You can always do better!
Static electric fields
• Novel sensing approach through electrical
fields in environment
• Infers the amount and type of body motion
anywhere on the body
• Orders of magnitude less power
consumption (3.3 µW!)
Some terminology
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Capacitor is a component that stores
electrical charges to be used at a later time
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Ground or electrical grounding is a safe
return path for electrical currents
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Capacitive coupling is the transfer of
energy within an electrical network by
storing electrical charge between circuit
nodes
Some terminology
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Static electric field: force field created
by the attraction and repulsion of electric
charges (the cause of electric flow). Does
not vary much (static)
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Exposure to a static electric field does not
produce a significant field inside the body,
but instead leads to the build up of electric
charges on the body surface
•
How does this actually
work?
Measure voltage across
capacitor
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One side of the capacitor
is connected to the body, and
the other side of the
capacitor is a small local
ground on the sensor board
•
Both capacitor are connected
to ground
How does this actually
work?
• Derive the relationships
between physical changes
(i.e., body movement) and
the sensed voltage (Vs).
How does this actually
work?
• E.g. user waves arm with sensor on leg
• None of local ground on the sensor or any
objects in the environment move
• However, Cb will change causing change in Vs
How does this actually
work?
• What if sensor is attached to moving body part?
• Body movement changes Cb, but also large changes
in Cr
• Subtract both for Vs
Video demo
Evaluation: how well
does it work?
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6 users (3 male, 3 female)
Different actions: (1) rest, (2) typing, (3) using computer
mouse, (4) small arm movements, (5) walking, (6) jogging
Each user performs actions 9 times in 2 locations (indoor)
Each action carried out in 5 second period
Carried out in random order
Evaluation: how well
does it work?
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Data collected classed into 4 groups: (1) rest, (2) hand and
finger movements, (3) small arm movements, (4) full body
movements
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Use simple thresholds to compute performance of
predictions
Construct ROC curves
ROC curves
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Measure performance of binary classifier
Vary cutoff and compute TPR, FPR
Applications
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Threshold-based wakeup for accelerometer
Since simple threshold cant distinguish finger/arm
movements very well
Wake-up must be done with higher levels of movement
Body motion
classification
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Use machine learning to classify signals into body motions
Considered 4 actions only: (1) rest, (2) small arm
movements, (3) walking, and (4) jogging
EF signal should be similar to that of accelerometers, so
authors used accelerometer features
Body motion
classification
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k-Nearest neighbor classification (k=1), 6 features
Power spectral density: describes how the power of a signal
is distributed over the different frequencies
It shows at which frequencies variations are strong and at
which frequencies variations are weak
Frequency-domain features: (1) the median power (2) and
median frequency
Body motion
classification
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Additional features
(3) standard deviation in 5s window
(4) zero-crossings of the derivative (i.e. peaks) in signal
(5) number of high magnitude, rapid changes in signal
(threshold absolute value of derivative)
(6) magnitude of first peak
Body motion
classification
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3-fold cross validation
Show ~92% accuracy
Conclusion
Discussion
body motion classification, the authors
• For
tested 4 gestures. Is this is enough? What
other gestures could have been added
Discussion
authors state that they could not use
• The
simple thresholding for finger/arm-based
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accelerometer wake-up.
Do you think modeling finger/arm signals in a
more complex manner (e.g. machine learning)
would have been able to differentiate?
Discussion
introduction, the authors state that
• Intheythetested
their method in multiple
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environments (indoors, outdoors, faraday
cage, field)
They did not show any results to support this
They mentioned their method works in a
large open field at least 0.6 km from power
lines. Is this practical?
Discussion
of leveraging accelerometer features?
• Benefits
Would generating your own custom adapted
set of training features provide more accurate
results?
Discussion
• k=1 used for kNN classifier
more training data and larger values
• Would
for k would improve accuracy?
Discussion
applications are pedometers and
• Suggested
gait analysis (animal locomotion). Do you
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think it is sensitive enough for more
extensive applications (gaming, computer
controllers)? Why?
Can you think of additional applications?
Discussion
you think the directionality of body
• Do
movements would have an impact on the
voltage signal
THANK YOU :)