Hello, everyone
My name is Ruiyang Gao from Peking University
Today, I am honored to present my work
entitled 'Towards Position-Independent Sensing
for Gesture Recognition with WiFi'.
Gesture is a user-friendly way
to easily convey Information.
which has become an important topic
for HCI to support the various application,
such as smart home control games and AR .
Among different gesture recognition approaches,
Wi-Fi-based sensing approaches re-use of widely deployed Wi-Fi devices
that can perform sensing task.
By analyzing the Channel State Information (CSI) of Wi-Fi devices
The behavior of users hand are speculated.
Despite the progress achieved,
there is a fundamental problem called position-dependency problem.
The problem is that signal patterns vary significantly different
for a same gesture with a different positions
As shown in the figure,
user draws digit ‘2’ with different locations and orientations.
The patterns of received CSI variations are very distinctive.
Limited by this issue,
the Existing Pattern-based and Physical Feature based approaches
can not recognize gestures in position-independent manner.
How to relieve the position-dependency problem?
The key of our approach is that
instead of observing hand motions from the transceiver view
we recognize gesture from the hand-oriented view.
A gesture can be consistently characterized
by how the hand moves relative to its previous position.
Such a hand-oriented view can provide consistent and distinguished representations for different gestures
which are relevant to the location and orientation of hand
We are now first talk about how to extract features from the hand-oriented view.
Then we will explain how to recognize gesture with the feature.
We observe that the changing pattern of the
moving direction of the hand keep consistent
in different positions.
As the example user draw digit '2' with different positions and orientation,
the changing patterns of the moving direction keep consistent.
Therefore, we develop a novel feature named
Motion Navigation Primitive (MNP) to
capture the changing pattern for gesture recognition.
The problem is , how to extract the consistent MNP from the raw signals.
To answer this question,
we now think about how to characterize the moving direction of the hand.
The velocity of hand can be projected into
two velocity components along any non-parallel axis.
As shown in the figure,
the ratio of two velocity components are monotonous with the moving directions.
Therefore we calculate the ratio to capture the changing pattern of moving directions
How to obtain projected velocity components?
The velocity component can be geometrically connected with the reflection path changes
on one pair of transceivers.
Utilizing two pairs of transceivers,
we obtain two velocity components and calculated their ratio.
How to estimate the reflection path changes?
Reflection path is physically connected
with the dynamic phase of CSI signals.
Each time the reflection path changes by one wavelength,
the dynamic phase changes 2 pi.
Therefore, we extract the dynamic phase changes
to estimate the reflection path changes.
The MNP can be extracted by the ratio of dynamic phase changes
from two pairs of transceivers.
Now we explain how to use the MNP for identifying gestures.
The mnp capture the changing patterns of the moving direction of gestures,
which is sufficient for position-independent sensing.
The slopes of MNP are correlated with the speed of the direction changes.
Faster MNP changes, greater the slope will be.
And the increasing or decreasing trends of MP are correlated with a clock directionality.
A complicated gesture can be decomposed into a combination of strokes,
which is line, arc and corner.
Each type of strokes corresponds to different moving direction changing speed.
Their MNP patterns are also distinguishable.
The temporal sequence of strokes can acts as the representation,
for gestures recognition.
We build a prototype called WiGesture to verify our idea.
The system consists of three modules
to recognize gestures in real-time.
The input gesture is identified by comparing its temporal sequence with the reference.
We run with gesture on three different gesture sets,
including basic gestures, digital gestures, and conducting gestures.
The average recognition accuracy is more than 92% high
We made three contributions in this work.
We propose a hand oriented view based strategy
to relieve the position-dependency problem.
We design a concrete position-independent feature MNP,
which captures the changing pattern of moving direction for gesture recognition.
We build our protype system to validate our methods
Thank you