Wi-ATCN: Attentional Temporal Convolutional Network for Human Action Prediction Using WiFi Channel State Information

Zhu, Aichun; Tang, Zhonghua; Wang, Zixuan; Zhou, Yue; Chen, Shichao; Hu, Fangqiang; Li, Yifeng

doi:10.1109/jstsp.2022.3163858

Cited by 16 publications

(4 citation statements)

References 36 publications

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“…The paper [9] proposes an activity recognition model based on a three-layer bidirectional long-and short-term memory network fused with attention to classify and recognize six activities in the open dataset in the paper [10] , and the experimental results show that the recognition rate can reach 96%. The paper [11] proposed an attentional temporal convolutional network by extracting continuous CSI time-domain features, and verified its advanced recognition rate in multiple datasets. The paper [12] proposed a bidirectional long and short-term memory network and attention mechanism to obtain features in both directions from the original CSI sequence and experimentally verified its performance due to classification algorithms such as HMM.…”

Section: Introductionmentioning

confidence: 89%

Human activity recognition based on CSI in multiple scenarios

Zhang

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Due to the popularity of commercial WiFi devices prompting the use of WiFi devices to achieve indoor human activity recognition(HAR) has become possible, HAR plays an important role in human-computer interaction, virtual reality and meta-universe, human walking, running and other activities will have an impact on the propagation of WiFi signals, by analyzing the information of the affected channel state, WiFi-based HAR can be achieved. In this paper, human activity states of three environments are classified, firstly by Hampel filtering to remove the anomaly noise, after that by DWT to further reduce the noise interference, then by extracting the statistical features in the time-frequency domain, and finally by random forest classification to complete the classification recognition. Through experimental verification, the method proposed in this paper can achieve 92% and 91% recognition rate in laboratory scene and corridor scene, which is better than the traditional algorithm recognition rate. It can also achieve 89% accurate recognition rate in non-line-of-sight(NLOS) environment, which also confirms the robustness of the proposed algorithm to the environment.

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Section: Introductionmentioning

confidence: 89%

Human activity recognition based on CSI in multiple scenarios

Zhang

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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“…WiAct [28] used the correlation between torso movement and amplitude information in channel state information to classify different activities, and used an extreme learning machine to organize activity data. Wi-ATCN [29] combined causal and dilated convolution to ensure the integrity of CSI features, using self-attention mechanisms to obtain the most representative features of human behavior. ImgFi [30] converted CSI into images and used a convolutional neural network to recognize CSI images, which received a high recognition rate and reduced the complexity of the model.…”

Section: Comparison Of Different Modelsmentioning

confidence: 99%

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion

Niu,

He,

Fang

et al. 2024

Sensors

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The recognition of human activity is crucial as the Internet of Things (IoT) progresses toward future smart homes. Wi-Fi-based motion-recognition stands out due to its non-contact nature and widespread applicability. However, the channel state information (CSI) related to human movement in indoor environments changes with the direction of movement, which poses challenges for existing Wi-Fi movement-recognition methods. These challenges include limited directions of movement that can be detected, short detection distances, and inaccurate feature extraction, all of which significantly constrain the wide-scale application of Wi-Fi action-recognition. To address this issue, we propose a direction-independent CSI fusion and sharing model named CSI-F, one which combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Specifically, we have introduced a series of signal-processing techniques that utilize antenna diversity to eliminate random phase shifts, thereby removing noise influences unrelated to motion information. Later, by amplifying the Doppler frequency shift effect through cyclic actions and generating a spectrogram, we further enhance the impact of actions on CSI. To demonstrate the effectiveness of this method, we conducted experiments on datasets collected in natural environments. We confirmed that the superposition of periodic actions on CSI can improve the accuracy of the process. CSI-F can achieve higher recognition accuracy compared with other methods and a monitoring coverage of up to 6 m.

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“…The domain discriminator is designed to identify the environment where activities are recorded, forcing the feature extractor to produce environment-independent activity features. Zhu et al [40] combined casual and dilated convolution to implement a temporal convolution network.…”

Section: Human Action Recognition Based On Wifi Channel State Informa...mentioning

confidence: 99%

Critical Analysis of Data Leakage in WiFi CSI-Based Human Action Recognition Using CNNs

Varga

2024

Sensors

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WiFi Channel State Information (CSI)-based human action recognition using convolutional neural networks (CNNs) has emerged as a promising approach for non-intrusive activity monitoring. However, the integrity and reliability of the reported performance metrics are susceptible to data leakage, wherein information from the test set inadvertently influences the training process, leading to inflated accuracy rates. In this paper, we conduct a critical analysis of a notable IEEE Sensors Journal study on WiFi CSI-based human action recognition, uncovering instances of data leakage resulting from the absence of subject-based data partitioning. Empirical investigation corroborates the lack of exclusivity of individuals across dataset partitions, underscoring the importance of rigorous data management practices. Furthermore, we demonstrate that employing data partitioning with respect to humans results in significantly lower precision rates than the reported 99.9% precision, highlighting the exaggerated nature of the original findings. Such inflated results could potentially discourage other researchers and impede progress in the field by fostering a sense of complacency.

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Wi-ATCN: Attentional Temporal Convolutional Network for Human Action Prediction Using WiFi Channel State Information

Cited by 16 publications

References 36 publications

Human activity recognition based on CSI in multiple scenarios

Human activity recognition based on CSI in multiple scenarios

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion

Critical Analysis of Data Leakage in WiFi CSI-Based Human Action Recognition Using CNNs

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