Hello everyone, this is Xinyi Li, a PhD student from northwest University, now I gonna
present our work, CrossGR： Accurate and Low-cost Cross-target Gesture Recognition Using Wi-Fi.
Human gesture and activity recognition underpins many real-world applications like health
care，fitness tracking and safety authentication.
Among all existing device-free gesture recognition systems,
the Wi-Fi-based system is attractive due to the widespread deployment,
less privacy intrusive and no light condition, and no requirement of the on-body sensor for users.
Unfortunately, existing approaches have a fundamental drawback by requiring a
labour-intensive and time-consuming process of collecting training measurements to
characterize the wireless channel metrics.
Specifically, the Wi-Fi signals bounce off human body and reach the receiver and create a
multipath profile indicative of the human body and their activities.
Therefore, the wireless signal characteristics, such as CSI or RSSI
correlate to not only the human gestures
but also the user’s physical features –such as the heights, weights,
and body shape - and behavior habits.
Hence, a model trained on data samples collected from a small number of users is
unlikely to be effective for CSI signals collected
from others because of the potentially drastic changes in the CSI signals.
To elaborate on the impact of dataset bias, we test the performance of a gesture recognition
model in two scenarios. The first one is to train and test the model on data collected from the same
target users. The second scenario is to train and test the model on the different dataset.
As we can see from the diagram, all machine learning models perform poorly on unseen users.
The CNN is best-performing model, but its prediction
accuracy drops from 95% to 36% on previously unseen users.
This drawback limits the scale at which Wi-Fi based gesture recognition can be operated
Ideally, we would like to build a system from training data collected from a few users,
and the learned system can be applied to many other new users
while still achieve high recognition accuracy.
So, We present CrossGR. CrossGR is designed to
address the two aforementioned limitations of current cross-target recognition systems.
The first one is the labor-intensive and time-consuming process of
collecting training measurements. The second is the cross-target recognition.
So, first, how to avoid expensive training data collection processing?
Our solution is generating synthetic training data to complement those real-world samples.
So, we use the generative adversarial network.
Our GAN-based training data synthesizer consists of two models:
a generative model and a discriminative model.
The generator is trained to capture the underlying distributions of real data. The discriminator is
trained to estimate the probability that a sample comes from the real dataset.
However, the synthetic wireless signal is more easily distorted by noise during the
generation process. Compared with original Wi-Fi signals, the synthetic data is noisy. Noise
components retain even if the synthetic signal is refined using a least-square smoothing filter.
To make the generator better fit the original data distribution,
we add convolution operations to extract efficient features automatically. Figure C indicates that
the synthetic data is more fine-grained and the filtered data becomes smoother.
Therefore, we can populate our training data with many synthetic data,
covering the space far more finely than what can be achieved by real data samples.
Next, how to perform gesture recognition across different users without requiring
having prior knowledge of them is another important issue in our system.
To solve this problem, Cross G R uses user-agnostic
but gesture-related features from CSI to achieve cross-target recognition.
Firstly, we use a stack of shared convolutional layers as the feature extraction layers in base
network, which aims to learn general but informative representations of WiFi signals.
And then, we use a target-adaptive classifier to identify the gesture label to obtain the
common gesture-related features across different users.
However, if we are recognizing gesture without distilling and dispelling features,
there will be interfere the recognition accuracy.
So, we use the feature eliminating path, to guide the base network to focus on extracting
gesture-related features to feed into the target-adaptive classifier for downstream
gesture recognition. It aims to quantify if we feed
a specific set of features how will that affect the probability distribution. If the features
are largely gesture-independent, we would expect all gesture classes to have more or less the same
probability. If this is the case, it means the features used for classification do not provide
sufficiently discriminating information to distinguish different gesture classes.
Next, let me tell you how we implement our system.
In this work, we collected the CSI measurements of 15 gestures .
We conducted extensive gesture recognition experiments in three indoor environments.
We invited 60 volunteers to participate in our experiment.
The participants have different heights, weights, and somatotype.
We use t-SNE to conduct joint dimension reduction processing for the original Wi-Fi
signal and the synthetic data generated. It implies that GAN can fit the original Wi-Fi
signal distribution well and expand the diversity of data to a certain extent.
To evaluate the overall performance, we conduct two experiments when the number of
gestures is 10 and 15. The average accuracy is 87.4% and 82.6%. respectively. It implies
that our system can achieve high accuracy for cross-target gesture recognition.
This experiment studies the impact of the number of volunteers in the testing dataset
on the system performance. The figure shows that the system achieves high accuracy of 87.42%
when the number of testing users increases to 10. Overall,
these results demonstrate that Cross G R is robust to the growth of testing users number.
Next, we evaluate the impact of the amount of synthetic data added for training our system. It
demonstrates that the generated synthetic data can effectively improve the system accuracy.
To conclude, in this work, we introduce a novel target-adaptive scheme to extract
user-agnostic but gesture-related features from Wi-Fi signal.
Thank you very much for your attention, I am happy to take any questions.