Dance Action Recognition and Pose Estimation Based on Deep Convolutional Neural Network

Zhu, Fengling; Zhu, Ruichao

doi:10.18280/ts.380233

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Before starting DMA transmission, DMA request should be issued. When a signal meeting the trigger condition enters the data acquisition card, the logic circuit (FPGA or PLD) on the board will drive the ADC to start sampling [6][7][8]. The timing function of the universal timer is enabled during programming.…”

Section: Sports Information Acquisitionmentioning

confidence: 99%

Sports Teaching Action Recognition Based on Hybrid CNN-HMM

Xie,

Ding

2024

Advances in Transdisciplinary Engineering

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In order to automatically recognize abnormal actions in physical education teaching in intelligent monitoring system, a CNN and HMM based action recognition model is proposed. In this scheme, the camera and computer are used to capture and measure the target, and the preliminary features of the image acquired from the data are extracted. Then, hybrid CNN-HMM is introduced as the key technology and main method of behavior recognition, and principal component analysis is also used to reduce the dimension of the extracted feature parameters. Finally, the test set is input into the trained classifier for action recognition, which provides an auxiliary reference for teaching actions. The experimental results show that the system can accurately quantify the human movements in sports teaching, and the recognition accuracy of complex databases with different scenes is better than that of similar methods.

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Section: Sports Information Acquisitionmentioning

confidence: 99%

Sports Teaching Action Recognition Based on Hybrid CNN-HMM

Xie,

Ding

2024

Advances in Transdisciplinary Engineering

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“…Shalini, A et al [21] has discussed the Deep Learning of Dance Movements (DLPD) is a new approach to understanding and analyzing hip-hop dance movements from digital recordings. The goal of extracting key movement characteristics from such recordings is to understand better the nuances of hip-hop dance and how it is performed.…”

Section: Introductionmentioning

confidence: 99%

IoT based Dance Movement Recognition Model based on Deep Learning Framework

Ji,

Tian

2024

SCPE

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Deep Learning is becoming an emerging field in the Internet of Things (IoT) due to its ability to provide a comprehensive approach to automatic feature extraction and predictive modeling for analysis and decision-making. This paper introduces an IoT-based Dance Movement Recognition Model based on a Deep Learning Framework. The framework consists of a convolutional neural network (CNN) with a data-centric architecture to identify dance movements from the acquired data gathered by an IoT device. The IoT device collects 3D motion data captured by three accelerometers. Feature extraction is then done with the CNN architecture, resulting in a flattened matrix representing the movement. Subsequently, a Multi-Layer Perception (MLP) is used to classify the movements. The proposed system is experimentally evaluated on a standardized dataset of 16 dance steps with three-speed levels. The results show that our model outperforms state-of-the-art approaches in accuracy, evaluation time, and classification accuracy. The proposed model reached 90.74% accuracy, 87.12% precision, 83.78% recall and 84.39% F1-Score. The proposed model can serve as a basis for a reliable and intuitive system that can be used to monitor patient’s dance movements with accuracy.

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