Optimizing Features Quality: A Normalized Covariance Fusion Framework for Skeleton Action Recognition

Huang, Guan; Yan, Qiuyan

doi:10.1109/access.2020.3037238

Cited by 7 publications

(3 citation statements)

References 51 publications

(47 reference statements)

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“…The other mainstream category comprises methods based on human skeleton joints or motion trajectories. [33][34][35][36][37][38][39][40][41][42][43] Owing to their high computational efficiency and good recognition performance, these methods are increasingly attracting the attention of researchers. B. Su et al [33] proposed a biologically based hierarchical model using Kinect skeleton data.…”

Section: Computer-vision-based Harmentioning

confidence: 99%

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Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

Zhao,

Sun,

et al. 2023

Advanced Intelligent Systems

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Human activity recognition (HAR) has gained popularity in the field of computer vision such as video surveillance, security, and virtual reality. However, traditional methods are limited in terms of computations and holistic learning of human skeletal sequences. In this article, a new time‐series skeleton joint data imaging method is infused into an improved convolutional neural network to handle these problems. First, the raw time‐series data of 33 body nodes are transformed to red–green–blue images by encoding the 3D positional information to one pixel. Second, the LeNet‐5 network is enhanced by expanding the receptive field, introducing coordinate attention and the smooth maximum unit to improve smoothness and feature extraction. Third, the ability of coded images to express human activities is studied in various environments. It is shown in the experimental results that the method achieves an impressive accuracy of 98.02% in recognizing 25 daily human activities, such as running, writing, and walking. In addition, it is shown that the number of floating point operations, parameters, and inference time of the method are 0.08%, 0.47%, and 3.05%, respectively, of the average values for six other networks (including AlexNet, GoogLeNet, and MobileNet). The proposed method is thus a novel, lightweight, and high‐precision solution for HAR.

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Section: Computer-vision-based Harmentioning

confidence: 99%

“…-19 daily activities: hand clapping, knocking, etc. 95.4% G. Huang [40] (2020) Camera Skeleton information 1 -7 classroom activities: raising hand, sleeping, etc. 82.8% F. Angelini [41] (2020) Camera Skeleton information of the image.…”

mentioning

confidence: 99%

Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

Zhao,

Sun,

et al. 2023

Advanced Intelligent Systems

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“…To obtain more reliable numbers, normalized covariance can be obtained by dividing covariance by the product of the standard deviation of two vectors [24]- [26]. As shown in formula (7), the Greek letter ρ is used to express Pearson correlation coefficient.…”

Section: B Noise Filtering Of Training Datamentioning

confidence: 99%

Efficient Compound Activity Prediction Model Based on Bayesian Regularization Method for Candidate Drugs

et al. 2022

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In breast cancer diagnoses, antagonist compounds are used to resist α Subtype of Estrogen Receptor (ERα) bioactivity. However, those compounds are difficult to be obtained in the process of drug screening. In this paper, an efficient bioactivity prediction model is proposed with consideration of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties as an auxiliary prediction to achieve the minimization of the damage caused to human body whereas maximizing the efficacy of estrogen activity inhibitors. In the proposed prediction model, Pearson correlation analysis and Bayesian regularization algorithm under neural network are specifically used to train and analyze the data of bioactivity. To calculate the loss of neural network, cross-entropy and supervise learning are applied. The results show that the accuracy of our proposed compound prediction model can reach up to 92.2%, 94.3%, and 90.2% for Caco2, CYP3A4, and hERG, respectively. Moreover, the Area Under the Curve (AUC) value of our proposed model is 0.9752, which is 13-14% higher than that of SVM method. In addition, the Matthews Correlation Coefficient (MCC) can reach up to 0.93 ± 0.03, 37-40% higher than that of SVM method. Due to correlation analysis and valid data screening, our model can shorten the prediction time by 15%. Due to its advantages in accuracy and speed of prediction, this research can contribute to the upgrading of diagnosis and treatment of breast cancer.INDEX TERMS Deep neural networks, drug discovery, machine learning, virtual screening.

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Real-time spatial normalization for dynamic gesture classification

et al. 2021

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Optimizing Features Quality: A Normalized Covariance Fusion Framework for Skeleton Action Recognition

Cited by 7 publications

References 51 publications

Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

Efficient Compound Activity Prediction Model Based on Bayesian Regularization Method for Candidate Drugs

Real-time spatial normalization for dynamic gesture classification

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