scholarly journals ML-WiGR: A Meta Learning based Approach for Cross-Domain Device-Free Gesture Recognition

2021 ◽  
Author(s):  
Zhenyue Gao ◽  
Jianqiang Xue ◽  
Jianxing Zhang ◽  
Wendong Xiao
Keyword(s):  
Neural Networks ◽  
Gesture Recognition ◽  
Short Term Memory ◽  
Learning Rate ◽  
Personal Privacy ◽  
Basic Model ◽  
Cross Domain ◽  
Meta Learning ◽  
Free Gesture ◽  
Device Free

Abstract Accurate sensing and understanding of gestures can improve the quality of human-computer interaction, and has great theoretical significance and application potentials in the fields of smart home, assisted medical care, and virtual reality. Device-free wireless gesture recognition based on WiFi Channel State Information (CSI) requires no sensors, and has a series of advantages such as permission for non-line-of-sight scenario, low cost, preserving for personal privacy and working in the dark night. Although most of the current gesture recognition approaches based on WiFi CSI have achieved good performance, they are difficult to adapt to the new domains. Therefore, this paper proposes ML-WiGR, an approach for device-free gesture recognition in cross-domain applications. ML-WiGR applies convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks as the basic model for gesture recognition to extract spatial and temporal features. Combined with the meta learning training mechanism, the approach dynamically adjusts the learning rate and meta learning rate in training process adaptively, and optimizes the initial parameters of a basic model for gesture recognition, only using a few samples and several iterations to adapt to new domain. In the experiments, we validate the approach under a variety of scenarios. The results show that ML-WiGR can achieve comparable performance against existing approaches with only a small number of samples for training in cross domains.

Multi-person device-free gesture recognition using mmWave signals

2021 ◽  
Vol 18 (2) ◽  
pp. 186-199
Author(s):  
Jie Wang ◽  
Zhouhua Ran ◽  
Qinghua Gao ◽  
Xiaorui Ma ◽  
Miao Pan ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Free Gesture ◽  
Device Free

scholarly journals Inference of Brain States under Anesthesia with Meta Learning Based Deep Learning Models

2021 ◽  
Author(s):  
Qihang Wang ◽  
Feng Liu ◽  
Guihong Wan ◽  
Ying Chen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
General Anesthetics ◽  
Learning Models ◽  
Multi Stage ◽  
Meta Learning ◽  
Brain States ◽  
High Level

AbstractMonitoring the depth of unconsciousness during anesthesia is useful in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) Networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We used a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.

scholarly journals High Performance Gesture Recognition via Effective and Efficient Temporal Modeling

2019 ◽  
Author(s):  
Yang Yi ◽  
Feng Ni ◽  
Yuexin Ma ◽  
Xinge Zhu ◽  
Yuankai Qi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
High Performance ◽  
Short Term Memory ◽  
State Of The Art ◽  
Computational Cost ◽  
Temporal Modeling ◽  
Spatiotemporal Features ◽  
Public Datasets

State-of-the-art hand gesture recognition methods have investigated the spatiotemporal features based on 3D convolutional neural networks (3DCNNs) or convolutional long short-term memory (ConvLSTM). However, they often suffer from the inefficiency due to the high computational complexity of their network structures. In this paper, we focus instead on the 1D convolutional neural networks and propose a simple and efficient architectural unit, Multi-Kernel Temporal Block (MKTB), that models the multi-scale temporal responses by explicitly applying different temporal kernels. Then, we present a Global Refinement Block (GRB), which is an attention module for shaping the global temporal features based on the cross-channel similarity. By incorporating the MKTB and GRB, our architecture can effectively explore the spatiotemporal features within tolerable computational cost. Extensive experiments conducted on public datasets demonstrate that our proposed model achieves the state-of-the-art with higher efficiency. Moreover, the proposed MKTB and GRB are plug-and-play modules and the experiments on other tasks, like video understanding and video-based person re-identification, also display their good performance in efficiency and capability of generalization.

scholarly journals WiGRUNT: WiFi-enabled Gesture Recognition Using Dual-attention Network

2021 ◽  
Author(s):  
Yu Gu ◽  
Xiang Zhang ◽  
Yantong Wang ◽  
Meng Wang ◽  
Zhi Liu ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Channel State ◽  
Attention Network ◽  
Fine Grained ◽  
Domain Specific ◽  
Cross Domain ◽  
Independent Features ◽  
Device Free ◽  
Domain Independent

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don't know ``\emph{where to look}'' and ``\emph{when to look}''. To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

scholarly journals Deep Learning Approach for Prediction of Critical Temperature of Superconductor Materials Described by Chemical Formulas

2021 ◽  
Vol 8 ◽  
Author(s):  
Dmitry Viatkin ◽  
Begonya Garcia-Zapirain ◽  
Amaia Méndez-Zorrilla ◽  
Maxim Zakharov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Critical Temperature ◽  
Short Term Memory ◽  
Learning Rate ◽  
Coefficient Of Determination ◽  
Chemical Formula ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

This paper proposes a novel neural network architecture and its ensembles to predict the critical superconductivity temperature of materials based on their chemical formula. The research describes the methods and processes of extracting data from the chemical formula and preparing these extracted data for use in neural network training using TensorFlow. In our approach, recurrent neural networks are used including long short-term memory layers and neural networks based on one-dimensional convolution layers for data analysis. The proposed model is an ensemble of pre-trained neural network architectures for the prediction of the critical temperature of superconductors based on their chemical formula. The architecture of seven pre-trained neural networks is based on the long short-term memory layers and convolution layers. In the final ensemble, six neural networks are used: one network based on LSTM and four based on convolutional neural networks, and one embedding ensemble of convolution neural networks. LSTM neural network and convolution neural network were trained in 300 epochs. Ensembles of models were trained in 20 epochs. All neural networks are trained in two stages. At both stages, the optimizer Adam was used. In the first stage, training was carried out by the function of losses Mean Absolute Error (MAE) with the value of optimizer learning rate equal to 0.001. In the second stage, the previously trained model was trained by the function of losses Mean Squared Error (MSE) with a learning rate equal to 0.0001. The final ensemble is trained with a learning rate equal to 0.00001. The final ensemble model has the following accuracy values: MAE is 4.068, MSE is 67.272, and the coefficient of determination (R2) is 0.923. The final model can predict the critical temperature for the chemistry formula with an accuracy of 4.068°.

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