Dance Action Research Articles

Evaluation of hand-eye coordination fitness test performance in folk dance

Hand-eye coordination refers to the ability to perform tasks that require the simultaneous use of the hands and eyes, such as using visual-spatial perception to guide hand movements. An intricate comprehension of the brain’s functioning is vital for individuals to possess hand-eye coordination, which necessitates the integration of visual and motor abilities. The study assessed the level of hand-eye coordination fitness during folk dance. This study employed a quantitative methodology, with researchers utilizing a quasi-experimental approach evaluating the hand-eye coordination of participants in folk dance. This study comprised of seventh-grade students during the academic year 2023-2024. Researchers noted that individuals without previous performance experience encountered challenges in executing the dance action, while those who engaged in practice found it effortless. The researchers discovered a statistically significant disparity in the pretest and post-test scores of the respondents. The findings indicated that the respondents’ hand-eye coordination level may be improved and cultivated when executing a folk dance by diligently practicing the given pattern. The researchers suggested the utilization of the Drop Stick Game, Head Shoulder Knees and Ball, Chest Overhead Pass, and Clap-Catch Game. These four developmental exercises enhance hand-eye coordination and optimize performance. Additionally, it will aid students in cultivating hand-eye coordination, particularly during performances.

Remote Dance Action Correction Based on EM and Min-min Algorithms

Remote Dance Action Correction Based on EM and Min-min Algorithms

Analysis of Mechanical Damage in Dance Training Under Artificial Intelligence Behavior Constraints

This study focuses on the application of artificial intelligence behavior constraints in the analysis of mechanical injuries in dance training, aiming to accurately analyze and effectively prevent mechanical injuries during dance training through the introduction of artificial intelligence technology. Dance, as a highly dependent art form on physical skills, often comes with a certain risk of mechanical injury during its training process. In this study, we first reviewed the relevant theories of mechanical injuries in dance training and analyzed the inherent relationship between dance movements and mechanical injuries. Subsequently, we utilized artificial intelligence technology to conduct behavior constraint analysis on the dance training process. By constructing a dance action recognition model, we achieved real-time monitoring and evaluation of dance training actions. On this basis, we further utilize the principles of mechanics to quantitatively analyze dance movements and extract key factors that affect mechanical damage. Through in-depth analysis and comparison, this study found that mechanical injuries in dance training are mainly influenced by various factors such as movement standardization, training intensity, and individual differences. We applied the theory of sports biomechanics to study sports dance injuries and analyzed the causes of athlete injuries. By exploring more scientific training methods and means, the correlation coefficients between main joint muscle strength, proprioception, tibialis anterior muscle imbalance response time, and rotational stability were measured to study proprioception training suitable for sports dance, adhering to the principle of gradual progression. In future sports dance rotation training, corresponding training should be carried out according to the characteristics of different rotation steps, providing reference for strengthening leg training and ankle training.

Entertainment type robots based on machine learning and game teaching mode applied in dance action planning of art teaching

We studied the use of machine learning models for training dance action models, collected dance action data, annotated and classified it, and established a training set for dance action models. We trained the training set to learn the feature representation and pattern recognition capabilities of dance actions. Through training and tuning the model, a model that can accurately recognize and generate dance movements was obtained. Evaluate the similarity between two dance movements and select the appropriate dance movements to form a smooth dance sequence. A planning algorithm was designed based on the kinematic and dynamic characteristics of robots to generate dance action paths suitable for the robot’s body conditions. Considering factors such as joint limitations, body stability, and smooth movement of the robot, generate a reasonable dance motion path while ensuring safety. Through on-site testing and data analysis of the system, it has been verified that it can effectively generate diverse and expressive dance movements, bringing a unique viewing experience to entertainment venues.

Influence of social robots and digital media based on edge computing on dance performance entertainment environment

This article proposes a new social robot system that achieves robot dance creation through dance image feature extraction and dance action imitation, thereby promoting the development of digital media dance entertainment culture. This paper uses the edge computing model, and on the basis of the robot’s own computing power, puts some computing tasks related to image processing and action imitation down to the edge of the network to improve the real-time and fluency of dance performance. The study utilized image feature extraction technology to analyze and process dance images, achieving the imitation of dance movements by robots. By imitating and innovating dance movements through robots, unique forms and content can be provided for digital media dance performances. Digital media technology provides rich means and forms of expression, which can make robot dance performances more creative and artistic.

Simulation of artificial intelligence robots in dance action recognition and interaction process based on machine vision

Artificial intelligence robots based on machine vision have shown great potential in dance action recognition and interaction. In order to perform action recognition, a large amount of dance action video data was collected as the basis for training and testing the model. In motion recognition, various computer vision techniques are used to extract motion features from images or videos to capture key information of dance movements. Machine learning technology was adopted to construct an action classification model. By using the data collected in the early stage and the extracted features, an efficient and accurate classifier has been studied and trained. It can classify the input dance actions into different categories, segment continuous video frames into different action segments, and ensure accurate differentiation of different dance actions during the recognition process, avoiding misjudgment or confusion. By accurately capturing and analyzing the posture information of the human body, we can better understand and simulate dance movements, and enhance the performance and realism of artificial intelligence robots in dance interactions. The experimental results show that the system can accurately recognize various dance movements and achieve good interaction with users.

Design and application of data security privacy protection technology in dance action sequence arrangement and display system

In view of the excessive consumption of difficult resources in traditional dance choreography, insufficient data in choreography and data leakage crisis in the system, this paper puts forward artificial intelligence technology to study the protection of dance choreography and system data security and privacy. In this paper, the directed graph neural network is used to intelligently arrange the dance movements, and the Hidden Markov Model (HMM) is used to process the dance scene data so that the output data is highly similar to the original data. In the protection of data security and privacy, the method of data encryption is used, and the encrypted data needs to be decrypted by algorithm for reading. The experimental results show that the more complete the dance movement display, the higher the dance recognition rate, and the recognition rate of the dynamic light projection algorithm is greater than 90 % regardless of several sets of dance movements, while the recognition rate of the traditional light projection algorithm is not up to the standard regardless of several sets of movements, and the recognition rate of the point cloud segmentation method is higher than 90 % only in 3–4 sets of dance and 5–6 sets of dance movements.

Emotion Analysis and Expression Algorithm of Dance Action Based on Machine Learning

Emotion analysis and expression algorithms represent a pivotal frontier in the intersection of artificial intelligence and human-computer interaction. These algorithms aim to decode and understand human emotions from various modalities such as text, speech, facial expressions, and physiological signals. This paper introduces the Context-Based Rough Sugeno Fuzzy (CBRSF) model tailored for emotion analysis and expression algorithms in the context of dance actions. With machine learning techniques, the CBRSF model integrates contextual information, rough set theory, and Sugeno fuzzy logic to accurately analyze and express emotions conveyed through dance movements. the power of machine learning techniques, the CBRSF model integrates various components, including contextual information, rough set theory, and Sugeno fuzzy logic, to provide a comprehensive framework for emotion analysis and expression. One of the key strengths of the CBRSF model lies in its ability to incorporate contextual information surrounding dance movements. Emotions conveyed through dance are often influenced by factors such as choreographic context, music, and cultural background. By integrating contextual cues into the analysis process, the CBRSF model can better capture the nuanced emotional nuances embedded within dance performances. The CBRSF model utilizes rough set theory to handle uncertainty and imprecision inherent in emotion analysis. Dance movements can be inherently ambiguous, making it challenging to accurately categorize the associated emotions. Rough set theory provides a principled framework for managing this uncertainty, allowing the CBRSF model to make informed decisions even in situations where data may be incomplete or inconsistent. Through comprehensive experimentation and evaluation, our proposed model achieves an emotion recognition accuracy of 98% across diverse dance action datasets, surpassing existing methods by 10.2%. Moreover, the CBRSF model enables nuanced emotion expression by dynamically adjusting dance movements based on real-time emotional cues.

FCTNet: Fusion of 3D CNN and transformer dance action recognition network

Folk dance is an important intangible cultural heritage in China. In the environment where movement recognition technology is widely used, there is still no research field on the protection and inheritance of folk dance culture. In order to better protect and inherit the minority dance, screening the typical movements of 5 types of minority dance, through the dance video frame processing, obtain the key movements of 19 class dance sequence, build the national dance typical action data set, put forward a 3D CNN fusion Transformer national dance recognition network model (FCTNet), the recognition rate of 96.7% in the experiment. The results show that the construction method of the folk dance data set is reasonable, the identification model has good performance for the classification of folk dance, and can effectively identify and record the folk dance movements, which also makes new contributions to the digital protection of folk dance.

Dance Action Capture and CAD Design Based on Big Data Algorithm Optimization

Dance Action Capture and CAD Design Based on Big Data Algorithm Optimization

Application of motion capture technology based on wearable motion sensor devices in dance body motion recognition

In recent years, people have been committed to studying how to recognize human behavior through the use of wearable devices and understand the intentions expressed by human actions, which has also attracted much attention in human-computer interaction. At present, research on dance mainly relies on computer simulation, while wearable devices can more realistically reflect the details and dynamic processes of dance movements. In order to realize the recognition of dancing human movements, this paper proposes a system that combines Motion capture technology and wearable devices, and uses wearable devices for recognition. Before the experiment began, we established a process for collecting and preprocessing dance action data. On the basis of in-depth research and analysis of Motion capture technology, we have successfully constructed a human Motion capture data-driven system, which can obtain the data needed by the human body to dance through wearable devices. After experimental verification, the system designed in this article performs excellently in recognizing human movements in dance. The recognition results not only contribute to dance training and correcting errors, but also gradually become an important way for dance learning, providing new ideas for dance learning and inheritance.

The influence of acrobatic exercises on the professional training of students in the field of choregraphic art

In this article, the problem of training the professional skills of students in the field of Choreographic art is addressed. At the same time, the presented article aims to inform specialists in the field regarding the training of skills through the use of acrobatic exercises. The efficiency of the process of acquiring dance actions is determined both by the possibilities of manifesting physical abilities and by the capacity of the subjects to capitalize on their artistic and aesthetic valences, influencing each other. Awareness of the importance of developing physical qualities creates necessary conditions for the dancer’s psychomotor development and constitutes a basis for the assimilation of professional techniques.

Dance movement error correction based on decision tree algorithm

Abstract In an environment where action recognition technology is widely used, dance action recognition technology has also received extensive attention. Due to the complexity of dance movements, there will be a large number of errors in dance movement recognition. Therefore, this paper uses the decision tree algorithm to optimize dance movement recognition. In this paper, the general framework of dance movement recognition is first constructed, and the human body features are extracted and calculated by using the Laban motion analysis method for the changes in the joints of dance movements. Then, use the decision tree algorithm to classify and identify the dance movements, and use it to optimize the self-obscuring motion information repair algorithm to solve the problem of dance movement occlusion and error, and complete the construction of the dance movement recognition model based on CNN network. Empirical analysis shows that the model method's Top-1 and Top-5 accuracy are both 84.7% and 89.32%, respectively. The average probability of incorrect recognition, error recognition, and correct recognition of the optimized dance action recognition in this paper is 8.84%, 3.78 and 87.38 respectively. The average recognition correctness is improved by 12.02%, which indicates that the effect of recognition of the optimized model of dance action masking and error correction in this paper is excellent. It has achieved a better construction in the field of dance action error correction.

Research on the optimization of emotion expression movement based on cognitive computing in dance creation

Abstract Cognitive computing technology realizes the processing of complex data and problems by simulating the human cognitive way, which is inspiring for the optimization of emotional expression in dance creation. In this paper, we propose a cognitive computing-based model for optimizing dance action emotions, using the improved 3DCNN model for dance action recognition and then CNN-BLSTM based on deep and shallow feature fusion for dance emotion recognition. The experimental results show that the students in the experimental group improved their movement emotion performance by 117% compared with the control group. Their professional level score was 8.42 points higher than that of the control group, the expert assessment score reached 43.98 points, and 82.5% of the students showed interest in dance learning. It shows that the optimization method for expressing dance emotions in this paper has good effects.

Multi-person dance tiered posture recognition with cross progressive multi-resolution representation integration.

Recognizing postures in multi-person dance scenarios presents challenges due to mutual body part obstruction and varying distortions across different dance actions. These challenges include differences in proximity and size, demanding precision in capturing fine details to convey action expressiveness. Robustness in recognition becomes crucial in complex real-world environments. To tackle these issues, our study introduces a novel approach, i.e., Multi-Person Dance Tiered Posture Recognition with Cross Progressive Multi-Resolution Representation Integration (CPMRI) and Tiered Posture Recognition (TPR) modules. The CPMRI module seamlessly merges high-level features, rich in semantic information, with low-level features that provide precise spatial details. Leveraging a cross progressive approach, it retains semantic understanding while enhancing spatial precision, bolstering the network's feature representation capabilities. Through innovative feature concatenation techniques, it efficiently blends high-resolution and low-resolution features, forming a comprehensive multi-resolution representation. This approach significantly improves posture recognition robustness, especially in intricate dance postures involving scale variations. The TPR module classifies body key points into core torso joints and extremity joints based on distinct distortion characteristics. Employing a three-tier tiered network, it progressively refines posture recognition. By addressing the optimal matching problem between torso and extremity joints, the module ensures accurate connections, refining the precision of body key point locations. Experimental evaluations against state-of-the-art methods using MSCOCO2017 and a custom Chinese dance dataset validate our approach's effectiveness. Evaluation metrics including Object Keypoint Similarity (OKS)-based Average Precision (AP), mean Average Precision (mAP), and Average Recall (AR) underscore the efficacy of the proposed method.

Design of innovative teaching methods for classical dance based on group intelligence

Abstract Compared with image records, 3D dance scores can realize the demonstration and practice of classical dance teaching more accurately and efficiently. In this paper, the Li group skeletal representation model is used for Li algebra space mapping processing to learn action feature data, classify them, and complete classical dance action feature extraction. Then, the residual network model is used as the backbone network to construct a two-stream spatiotemporal residual network for action recognition by using two different temporal spans and fusing the results of the two-channel networks through side connections. Finally, the group intelligence algorithm group intelligence algorithm ACOR-SGD is designed to optimize the parameters of the convolutional neural network, and then the dance score generation model is integrated into the design of the innovative teaching method of classical dance for the application of the professional teaching of classical dance in colleges and universities. There is basically no significant difference between the pre-test scores of the experimental and control classes. After one semester of applying the innovative teaching method based on the generation of dance scores, the scores of the experimental class in the five items of basic pace mastery, movement fluency, stage presence, technical quality, and coordination of the music rhythm were improved by 4.6, 4, 4.28, 3.63, and 4.72 respectively, and the total score of 81.54 was much higher than that of the control class of 60.57 points posttest scores, and all were significant at the 0.01 level. This study explores innovatively and provides an effective path for integrating dance teaching with cutting-edge information technology, thus improving the quality and efficiency of dance teaching.

Feature extraction and classification of dance movements based on data mining

Abstract In recent years, the expansive utilization of data mining techniques has revolutionized various fields, including the analysis of dance videos. This study leverages data mining to meticulously capture and analyze dance movements, thereby facilitating the enhancement and correction of dancers’ techniques. Within the scope of this research, images of dance movements extracted from videos are subjected to preprocessing, which involves grayscaling and thresholding, to prepare them for further analysis. Building on these processed images, this paper introduces a novel multi-feature dance action recognition approach. This method integrates several distinct features—directional gradient histogram features, optical flow directional histogram features, and audio features—employing a linear weighting scheme within a multi-core learning framework. The efficacy of the proposed approach is demonstrated through its performance on the FolkDance dance dataset, where it achieves a 3.5% increase in fusion accuracy over the traditional Dance Style Identification (DSI) method. Additionally, when compared with the Multi-Feature Learning-Combined (MFL-C) method, our approach shows an improvement of 0.6% in fusion accuracy. This research establishes a viable method for the recognition and classification of dance movements, laying a robust foundation for further inquiry and practical applications in this domain.

Machine Learning Based Analysis and Prediction of Emotional Expressions in Dance Movements

Abstract Dance movement is a powerful way to convey human emotions, and analyzing and predicting the emotional expression of dance movement through machine learning has become a hotspot in the field of artificial intelligence research nowadays. This paper employs the Seagull optimization algorithm to enhance the SVM classification model, laying the algorithmic groundwork for the research, and refines it to align with the research requirements. The classification of dance movements is accomplished by the nonlinear regression algorithm in the support vector machine regression algorithm, while the task of capturing dance movements is realized by means of Euler angles to describe the orientation, rotation matrices to transform vectors between different coordinate systems, and quaternions to optimize the Euler angles. Finally, this paper analyses and predicts the emotional expression of dance movements using a classification loss model (LSTMBO) and a W-RNN model that incorporates the weights of emotion words. In this paper, tests on the classification algorithm revealed that the algorithm's classification accuracy is above 90% for all datasets used in the research. Moreover, the performance and effectiveness of dance action capture are significantly better than other comparative algorithms. Simultaneously, this paper's algorithm achieves an accuracy of over 80% in predicting emotions expressed through dance movements. Numerous experiments have proven the effectiveness and superiority of the algorithm model in this paper, thereby promoting the research and development of the field.

Research on Cross-Cultural Digital Communication Strategy of Musical Theatre Dance Based on Pattern Recognition Technology

Abstract The dissemination and development of musical theater promote each other; smooth dissemination can promote development, and comprehensive development can expand the time and space of dissemination. With the development of time, the effective digital communication strategy of musical theater has gradually become the direction of research. The study utilizes pattern recognition technology to combine sensors for action pattern recognition of musical theater dance and first develops a human motion model that meets the needs of musical theater dance action. The sensor system was designed and developed from three parts, such as acceleration measurement sensors, plantar pressure measurement shoes, and a data acquisition motherboard. On this basis, the pattern recognition method of this paper is combined with the musical to realize the improvement and optimization of the musical stage, and the digital communication strategy of the musical dance is proposed according to the audience communication model. The sensor-based pattern recognition method of this paper has reduced the average output error of accelerometers by another 60%, and the average output error of gyroscopes by 48% after the calibration of its sensors, and the recognition rates of the eight patterns oriented to the musical theater dance are all above 91%. Based on the digital communication strategy of this paper, the communication effect of the musical theater in the digital platform attention to the total number of people, the total number of viewers, and the revenue of the theater company increased by 150,000 people, 53,000 people, and 28,000 yuan, respectively. This study, based on pattern recognition of musical digital communication strategies in practice, achieved good results. It provides an effective path for the development and promotion of musical theater.

HFA-GTNet: Hierarchical Fusion Adaptive Graph Transformer network for dance action recognition

Dance action recognition is a hot research topic in computer vision. However, current skeleton-based action recognition methods face difficulties in capturing the adequate spatial structure and temporal variations of dance actions, resulting in lower recognition accuracy. In this paper, we propose a Hierarchical Fusion Adaptive Graph Transformer network (HFA-GTNet) for dance action recognition. A Hierarchical Spatial Attention (HSAtt) module is designed to extract different levels of spatial feature information from joint to parts to group, it can effectively learn high-order dependency relationships from local joints to global poses in dance actions. Secondly, to extract the joint variations in dance actions at different speeds, we have designed a Temporal Fusion Attention (TFAtt) module. This module learns the short-term and long-term temporal dependencies among joints across frames. Additionally, to capture the variations in motion patterns and dance styles among different dancers, we introduce an Adaptive Component (AdaptC). Finally, we evaluate our model on two self-built dance datasets, MSDanceAction and InDanceAction, and demonstrate its superior performance compared to other state-of-the-art methods in dance action recognition.