Deep Learning Approach for Emotion Recognition from Human Body Movements with Feedforward Deep Convolution Neural Networks

Santhoshkumar, R.; Geetha, M. Kalaiselvi

doi:10.1016/j.procs.2019.05.038

Cited by 55 publications

(17 citation statements)

References 9 publications

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“…CNN usually extracts spatial related elements from a fixed size range, which limits the observation scale, resulting in low image classification accuracy [22]. In order to improve the accuracy, it is necessary to extract multiscale spatial elements.…”

Section: Extraction Of Landscape Informationmentioning

confidence: 99%

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

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Section: Extraction Of Landscape Informationmentioning

confidence: 99%

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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“…They used a feedforward deep convolutional neural network architecture with different parameters to recognize emotional states from whole body motion patterns. Experimental results show that the system has good recognition accuracy [ 5 ]. Wang et al's research puts forward a new radar-based recognition method of human body and limb movement, which takes advantage of the temporal sequence of movement.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Recognition of Sports Players’ Wrong Actions in view of Computer Pattern Recognition

Guo

2022

Computational Intelligence and Neuroscience

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In the context of the vigorous development of the sports industry and rapid technological innovation, the wrong actions of sports athletes can also be intelligently recognized. Human action recognition based on computer pattern recognition is becoming more and more popular and ubiquitous in life. This article aims to study how to recognize the human body based on the computer model and how to apply intelligent recognition to the wrong actions of sports athletes. The study of the application of intelligent recognition to the wrong actions of sports athletes is of great significance to sports athletes. This article proposes how to intelligently recognize the wrong actions of sports athletes based on computer pattern recognition. In the experiment in this article, wrong sports actions can cause a series of undesirable consequences, such as joint sprains and muscle damage. Among them, the proportion of joint damage caused by wrong actions has reached 24% and has been rising with the increase of the number of experiments and finally reached 35%, which shows that the probability is still very high. After the pull-up adopts intelligent recognition, the error of the pull-up action can be quickly identified and corrected in time, with the correct rate reaching 78%. Therefore, in order to reduce the physical damage caused by sports athletes’ wrong movements, it is necessary to study the intelligent recognition of sports athletes’ wrong movements. The recognition of wrong actions of sports athletes can be carried out through intelligent recognition based on 3D convolutional neural networks, which is of great significance to intelligent recognition.

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“…As is well known, human intelligence is not reduced only to the intellectual (Li and Deng 2018) or her body language and posture, which seems the most informative observations even realized from afar and from any angle of view (Santhoshkumar and Geetha 2019).…”

Section: Implementing Emotional Intelligencementioning

confidence: 91%

Bayesian Rationality Revisited: Integrating Order Effects

Uzan

2022

Found Sci

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Bayes' inference cannot reliably account for uncertainty in mental processes. The reason is that Bayes' inference is based on the assumption that the order in which the relevant features are evaluated is indifferent, which is not the case in most of mental processes. Instead of Bayes' rule, a more general, probabilistic rule of inference capable of accounting for these order effects is established. This new rule of inference can be used to improve the current Bayesian models of cognition. Moreover, it should play an essential role in the search for artificial emotional intelligence.

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Deep Learning Approach for Emotion Recognition from Human Body Movements with Feedforward Deep Convolution Neural Networks

Cited by 55 publications

References 9 publications

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

Intelligent Recognition of Sports Players’ Wrong Actions in view of Computer Pattern Recognition

Bayesian Rationality Revisited: Integrating Order Effects

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