Hello, everybody.
My name is Yingwei Zhang.
I am a research assistant from Institute of Computing Technology, Chinese Academy of Sciences.
I am very happy to introduce our recent work
A Feature Adaptive Learning Method for High-Density sEMG-Based Gesture Recognition.
This is the content of our report.
I will introduce the background, the proposed method,
the experimental analysis, and the conclusion, respectively.
Gesture recognition aims to recognize an individual's
meaningful movement expressions of hands, arms, or even body.
It is of utmost importance in many human-computer interaction applications,
such as medical rehabilitation, sign language recognition and virtual reality.
Currently, there are many existing gesture recognition solutions,
such as the Computer vision, the inertial measurement unit,
and the electromyographic based gesture recognition.
Among the various solutions for gesture recognition,
sEMG shows excellent potential in gesture recognition by directly sensing
and decoding human muscle activities.
Existing methods generally assume that the off-line training and the on-line testing data streams
have a fixed feature space.
However, this assumption does not hold in many real-world scenarios.
The decrement and the increment of electrodes
will cause changes in sEMG feature space.
To handle these kinds of changes,
one straightforward approach is to build a new model with additional electrodes.
However, this kind of approach is limited by some factors.
To address these challenges, we propose a novel feature
incremental and decremental ensemble learning method in our research, namely FIDE.
FIDE is composed of two main parts,
the spatial constraint weighting based stratified random forest and the adaptive perceptual methods.
In our research, we define the feature evolution process as:
The augmentation of electrodes is usually planned and expected;
However, the vanishment of electrodes is usually unexpected, caused by some
environmental factors, such as motion artifact, hair, and sweat on the surface of the skin.
These are more like the realistic process.
During the process of constructing the stratified random forest,
we define the concept of distribution difference,
which can be used to calculate the distance of two electrodes.
Then, we cluster different electrodes by the k-means based electrode clustering method. Finally,
we build the stratified random forest by constructing decision trees for each group of electrodes.
During the process of feature decrement,
we reconstruct the missing features
by calculating distribution difference and matrix sketch. During the process of feature increment,
we define the selection criteria for individual classifier and two different trees updating methods.
Then, we will introduce the experiment briefly.
We conduct experiment on the benchmark datasets and the real-world scenarios.
On the benchmark datasets,
we select three dataset and extract six kinds of different features.
The experimental results verify the effectiveness of FIDE.
We also design a system, namely McS,
to further verify the effectiveness of FIDE in real-world scenarios.
The experiments in real-world scenarios also verify the effectiveness of FIDE.
The last part is our conclusion.
sEMG based gesture recognition is widely studied in human-computer interaction area.
In this paper, we focus on expected feature increment and unexpected feature decrement challenges
in gesture recognition
and propose an adaptive ensemble learning method, namely FIDE.
FIDE defines two important concepts to build the stratified random forest
and continuously updates the existing model.
Comparative experiments on three benchmark datasets
and two real-world scenarios all show the effectiveness of FIDE.
That's all of our report. Thank you for your listening.