Machine learning model-based two-dimensional matrix computation model for human motion and dance recovery

Zhang, Yi; Zhang, Mengni

doi:10.1007/s40747-020-00186-9

Cited by 9 publications

(5 citation statements)

References 18 publications

(22 reference statements)

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“…If one wishes to modify the acquired data, it requires the performers to reperform it all over again, generating a large amount of work. With the application of digitalization in the field of folk dance, a technical solution for digital preservation and display of folk dance has emerged, which uses skeletal mask animation for virtual dance display based on the acquired movement data [9]. Folk dances from different regions vary greatly, with different costumes and dance forms, and even for minority dances from the same region, each dance is similar in style but also has its characteristics.…”

Section: Related Workmentioning

confidence: 99%

Application of Rotationally Symmetrical Triangulation Stereo Vision Sensor in National Dance Movement Detection and Recognition

Jiang

2022

Wireless Communications and Mobile Computing

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Information acquisition is an important branch of information science. It is the product of the development and cross-integration of traditional sensing technology and other multidisciplines. It is characterized by the high precision, high speed, integration, and intelligence of information acquisition. An important part of information acquisition, it studies how to acquire the geometric structure and scale information of objects in three-dimensional space. With the continuous development of the level, the acquisition of 3D information has become more and more important in scientific research and industrial production. This paper takes 3D information acquisition technology as the main line. From the perspectives of fusion, effectiveness, physical limit, and measurement efficiency, high-resolution 3D information acquisition techniques are studied. In this paper, the upper and lower limb movements of each behavioral segment are segmented according to the difference. An omnidirectional stereo vision sensor composed of a single camera and two secondary conical mirrors is designed, which solves the problems of large size, narrow field of view, and asynchronous image acquisition of traditional binocular vision sensors, effectively ensuring image perspective. The invariance of projection avoids image distortion caused by curved mirror imaging and reduces the difficulty of subsequent work. The three-dimensional Delaunay triangulation algorithm is used to replace the traditional Poisson reconstruction algorithm to generate the triangular mesh model. The triangular mesh model obtained by the multiview stereo reconstruction algorithm is relatively rough. This article uses Zbrush’s ZRemesher and geometric-divide functions to smooth and simplify the model. The results show that the accuracy of the algorithm proposed in this paper is as high as 89.25% in the motion recognition of body elements. The dynamic mapping method is used to map the texture of the triangular mesh model, so that the realism of the model reaches 91.23%.

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Section: Related Workmentioning

confidence: 99%

Application of Rotationally Symmetrical Triangulation Stereo Vision Sensor in National Dance Movement Detection and Recognition

Jiang

2022

Wireless Communications and Mobile Computing

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“…IoT-based dance movement recognition is a system that uses Internet of Things technology to recognize dance movements when a person is dancing. It uses sensors to detect motion and sends the data to an analysis engine that can recognize different moves and generate reports of the person's performance [1]. This system can be used for educating dancers, assessing a professional dancer's performance, and even for games.…”

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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“…These devices are expensive and have poor flexibility, which affects the execution and recognition of actions. In addition, for the problem of occlusion in human movements, the motion characteristics in different scenes have still not achieved satisfactory effects, which also limits the inheritance and protection of dance [ 5 ]. For motion data collection, Kinect depth vision sensor equipment has the advantages of high resolution of depth map, low cost, and ability to directly track the motion trajectory of human skeleton [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Graph Convolutional Neural Network for Dance Tracking and Pose Estimation

Zhang

2022

Computational Intelligence and Neuroscience

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Movement recognition technology is widely used in various practical application scenarios, but there are few researches on dance movement recognition at present. Aiming at the problem of low accuracy of dance movement recognition due to complex pose changes in dance movements, this paper designed an improved graph convolutional neural network algorithm for dance tracking and pose estimation. In this method, the spatial and temporal characteristics of motion are extracted from the skeleton joint diagram of human body. Then, GCN (graph convolutional neural) is used to extract potential spatial information between skeleton nodes. Finally, LSTM (long short-term memory) extracts the time series features before and after human actions as a supplement and performs late fusion of the prediction outputs of the two networks, respectively, to improve the problem of insufficient generalization ability of single network. The experimental results show that this method can effectively improve the accuracy of dance movement recognition in general movement recognition data set and dance pose data set. It has certain application value in dance self-help teaching, professional dancer movement correction, and other application scenarios.

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Machine learning model-based two-dimensional matrix computation model for human motion and dance recovery

Cited by 9 publications

References 18 publications

Application of Rotationally Symmetrical Triangulation Stereo Vision Sensor in National Dance Movement Detection and Recognition

Application of Rotationally Symmetrical Triangulation Stereo Vision Sensor in National Dance Movement Detection and Recognition

IoT based Dance Movement Recognition Model based on Deep Learning Framework

Improved Graph Convolutional Neural Network for Dance Tracking and Pose Estimation

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