Deep Adaptation Networks Based Gesture Recognition using Commodity WiFi

Han, Zijun; Guo, Lingchao; Lu, Zhaoming; Wen, Xiangming

doi:10.1109/wcnc45663.2020.9120726

Cited by 20 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This greatly reduces the need for training samples, but can improve recognition accuracy and convergence speed. Han et al [25] proposed a semi-supervised, fine-grained, deep-adapted network gesture recognition scheme (DANGR), and GAN is used to expand the dataset. The key idea is to adopt the domain adaptation based on the multi-core maximum mean difference scheme.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with DADA-AD by calculating similarity, DADA-AD can use a small number of the targets' label data for transfer learning. Han et al [25] used the results of classification to measure learning, and used the multicore maximum mean difference (MK-MMD) to measure the difference between domains, so as to provide a standard for the fusion of domain differences, thereby accelerating the efficiency of transfer learning. The need to calculate multiple kernel functions leads to poor model efficiency.…”

Section: Motivationmentioning

confidence: 99%

“…DADA-AD can more accurately measure the difference between the two domains, and use the embedder to map, so that the time to calculate the similarity is linear. Therefore, this paper proposes the dynamic associate domain adaptation learning using attention-based DenseNet (DADA-AD) scheme to improve the generalization ability, maximize domain confusion, and minimize classification loss for source and target domains, as shown in Figure 2, combining the advantage of [19,24,25] to achieve the identification result that is not affected by the environment.…”

Section: Motivationmentioning

confidence: 99%

See 2 more Smart Citations

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals

Chen

Chang

2021

Sensors

View full text Add to dashboard Cite

Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, background estimation, feature extraction, feature enhancement, and data augmentation in the data pre-processing stage. This paper considers the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled dataset of all of the CSI of human activity patterns. Then, the pre-trained model is transferred to the target environment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, a partial labeled dataset of the target domain is required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled dataset/unlabeled dataset, while the existing associate domain adaptation algorithm only allows target domains with the unlabeled dataset. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by an existing DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper. The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals

Chen

Chang

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The channel noises are implicitly included in H(f, t). Due to reflection, refraction or scattering, wireless signals propagate from TX to RX through multiple paths, resulting in multipath distortions [22]. Mathematically, CSI is the superposition of signals from all the propagation paths, which can be represented as:…”

Section: A Overview Of Csimentioning

confidence: 99%

“…Among these solutions, WiFi-based systems stand out as particularly promising due to the ubiquity, pervasive availability as well as privacy preservation. The development of WiFi Network Interface Cards (NICs), such as Intel 5300 and Atheros AR9580 series, has granted the researchers access to a fine-grained channel measurement from the physical layer [22], i.e., Channel State Information (CSI). CSI describes the detailed propagation of signals from the transmitter (TX) to the receiver (RX) through multiple paths at the granularity of Orthogonal Frequency Division Multiplexing (OFDM) subcarriers [23].…”

Section: Introductionmentioning

confidence: 99%

CentiTrack: Towards Centimeter-Level Passive Gesture Tracking with Commodity WiFi

Han¹,

Lu²,

Wen³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Gesture awareness plays a crucial role in promoting human-computer interface. Previous works either depend on customized hardware or need a priori learning of wireless signal patterns, facing downsides in terms of the privacy concern, availability and reliability. In this paper, we propose CentiTrack, the first centimeter-level passive gesture tracking system that works with only three commodity WiFi devices, without any extra hardware modifications or wearable sensors. To this end, we first identify the Channel State Information (CSI) measurement error sources in the physical layer process, and then denoise CSI by the complex ratio between adjacent antennas. Principal Component Analysis (PCA) is further adopted to separate the reflected signals from noises. Benchmark experiments are conducted to verify that the phase changes of denoised CSI are proportional to the length changes of dynamic path reflected off the hand. In addition, we adopt the Multiple Signal Classification (MUSIC) algorithm to estimate the Angle-of-Arrivals (AoAs) of dynamic paths, and then locate the initial position of hands with triangulation. We also propose a novel static componnets elimination algorithm for tracking correction by eliminating the components unrelated to motion. A prototype of CentiTrack is fully realized and evaluated in various real scenarios. Extensive experiments show that CentiTrack is superior in terms of tracking accuracy, sensing range and device cost, compared with the state-of-the-arts.

show abstract

Towards Cross Domain CSI Action Recognition Through One-Shot Bimodal Domain Adaptation

Zhang

Zhou

Luo

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Deep Adaptation Networks Based Gesture Recognition using Commodity WiFi

Cited by 20 publications

References 11 publications

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals

CentiTrack: Towards Centimeter-Level Passive Gesture Tracking with Commodity WiFi

Towards Cross Domain CSI Action Recognition Through One-Shot Bimodal Domain Adaptation

Contact Info

Product

Resources

About