Through-Wall Human Motion Recognition Based on Transfer Learning and Ensemble Learning

Chen, Pengyun; Guo, Shisheng; Li, Huquan; Wang, Xiang; Cui, Guolong; Jiang, Chaoshu; Kong, Lingjiang

doi:10.1109/lgrs.2021.3070374

Cited by 18 publications

(11 citation statements)

References 14 publications

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“…Chen et al put forward a multi-radar collaborative HAR model based on transfer and integrated learning to solve the view limitation in AR. Through experiments, the proposed model had higher recognition accuracy compared to the single-view radar fusion model [16]. To promote the accuracy of action combination training AR model, Jiang and Tsai proposed a model based on sequential minimal optimization model and artificial intelligence.…”

Section: Related Workmentioning

confidence: 98%

Algorithm for Skeleton Action Recognition by Integrating Attention Mechanism and Convolutional Neural Networks

Liu

2023

IJACSA

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An action recognition model based on 3D skeleton data may experience a decrease in recognition accuracy when facing complex backgrounds, and it is easy to overlook the local connection between dynamic gradient information and dynamic actions, resulting in a decrease in the fault tolerance of the constructed model. To achieve accurate and fast capture of human skeletal movements, a directed graph convolutional network recognition model that integrates attention mechanism and convolutional neural network is proposed. By combining spacetime converter and central differential graph convolution, a corresponding central differential converter graph convolutional network model is constructed to obtain dynamic gradient information in actions and calculate local connections between dynamic actions. The research outcomes express that the crosstarget benchmark recognition rate of the directed graph convolutional network recognition model is 92.3%, and the crossview benchmark recognition rate is 97.3%. The accuracy of Top-1 is 37.6%, and the accuracy of Top-5 is 60.5%. The cross-target recognition rate of the central differential converter graph convolutional network model is 92.9%, and the cross-view benchmark recognition rate is 97.5%. Undercross-target and cross-view benchmarks, the average recognition accuracy for similar actions is 81.3% and 88.9%, respectively. The accuracy of the entire action recognition model in single-person multi-person action recognition experiments is 95.0%. The outcomes denote that the model constructed by the research institute has higher recognition rate and more stable performance compared to existing neural network recognition models, and has certain research value.

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Section: Related Workmentioning

confidence: 98%

Algorithm for Skeleton Action Recognition by Integrating Attention Mechanism and Convolutional Neural Networks

Liu

2023

IJACSA

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“…In the hierarchical fusion layer, it is necessary to obtain concise representations of the strong features that appear in each dimension of the output feature G in the multilevel dependency interaction layer. Then, the attention mechanism is used to fuse the strong features to obtain the fusion feature C. The specific calculation is shown in Equation (12).…”

Section: Construction Of Answer Selection Model Of Medical Question A...mentioning

confidence: 99%

Medical education and artificial intelligence: Question answering for medical questions based on intelligent interaction

Chen

2024

Concurrency and Computation

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SummaryComputer assisted medical diagnosis technology is widely used in the field of medical assistance to assist doctors in making diagnostic decisions. But as the number of patients increases, the diagnostic pressure on doctors gradually increases, and more efficient computer‐aided medical diagnosis technology is needed to improve the accuracy of doctors' diagnosis. Today's computer‐based medically assisted diagnostic technologies suffer from the inability to fully simulate physical attributes and environmental factors, the large computational resources required for high‐precision models, the need for professional training for user operation, and the limited intuition for innovative design. For improving diagnostic efficiency, this study designs a medical Question answering intelligent interaction system in view of artificial intelligence algorithms. The system is constructed with an active interactive intelligent Q&A model consisting of a medical reasoning module and a medical examination recommendation module. Then, it uses local Bayesian network algorithm as the foundation to establish an intelligent strategy optimization network. And it puts forward the answer selection model of medical Question answering in view of hierarchical interaction for natural language processing tasks in the medical context. The performance test results show that when the diagnostic end threshold of the medical reasoning module is 0.8, the shortest diagnostic path is 3.33. When the diagnostic threshold is 0.85, the maximum length of the diagnostic path is 4.66, and the maximum difference between the diagnostic paths is 1.33, which is basically not affected by the diagnostic end threshold. The local Bayesian network algorithm can reduce the impact of noise features and extract more valuable information. The accuracy of the multilevel interactive answer selection model on the Stanford Natural Language Inference dataset without using external resources reached 89.2%. The ablation test results show that the overall accuracy of the model is 89.64%. The visualization results of the attention weight distribution test between interaction layers show that under different levels of interaction, the attention distribution will undergo significant changes.

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“…The probability vectors are fused by the method of maximum entropy (ME) [48], and the decision results are given. The whole comparison process is shown in Fig.…”

Section: Classification Comparison Experiments With and Without Data ...mentioning

confidence: 99%

TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition

Gao

Yang

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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To solve the problems of reduced accuracy and prolonging convergence time of through-the-wall radar (TWR) human motion due to wall attenuation, multipath effect, and system interference, we propose a multilink auto-encoding neural network (TWR-MCAE) data augmentation method. Specifically, the TWR-MCAE algorithm is jointly constructed by a singular value decomposition (SVD)-based data preprocessing module, an improved coordinate attention module, a compressed sensing learnable iterative shrinkage threshold reconstruction algorithm (LISTA) module, and an adaptive weight module. The data preprocessing module achieves wall clutter, human motion features, and noise subspaces separation. The improved coordinate attention module achieves clutter and noise suppression. The LISTA module achieves human motion feature enhancement. The adaptive weight module learns the weights and fuses the three subspaces. The TWR-MCAE can suppress the low-rank characteristics of wall clutter and enhance the sparsity characteristics in human motion at the same time. It can be linked before the classification step to improve the feature extraction capability without adding other prior knowledge or recollecting more data. Experiments show that the proposed algorithm gets a better peak signal-to-noise ratio (PSNR), which increases the recognition accuracy and speeds up the training process of the back-end classifiers.

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Through-Wall Human Motion Recognition Based on Transfer Learning and Ensemble Learning

Cited by 18 publications

References 14 publications

Algorithm for Skeleton Action Recognition by Integrating Attention Mechanism and Convolutional Neural Networks

Algorithm for Skeleton Action Recognition by Integrating Attention Mechanism and Convolutional Neural Networks

Medical education and artificial intelligence: Question answering for medical questions based on intelligent interaction

TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition

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