Detecting and Visualizing Stops in Dance Training by Neural Network Based on Velocity and Acceleration

Jin, Yuuki; Suzuki, Genki; Shioya, Hiroyuki

doi:10.3390/s22145402

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…In the paper "Detecting and Visualizing Stops in Dance Training by Neural Network Based on Velocity and Acceleration", the authors discuss how they developed a system in which they attached 18 small sensors to various parts of the dancer's body to record dance movements using motion capture technology [2]. A neural network model was employed to analyze and detect stops in dance movement.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Mobile Application to Analyze Dance Movements and Provide Feedbacks using Media Pipe and OpenCV

Zhu,

Tran

2024

Artificial Intelligence and Big Data

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Motion Mentor is a mobile application designed to address a challenge faced by beginner dancers in improving their dancing technique-the development of improper movements and habits when practicing without a teacher’s guidance. Therefore, Motion Mentor offers real-time posture correction and personalized feedback [5]. This method involves using the Mediapipen pose-detection AI model for real time posture detection, combined with advanced algorithms for accurate dance analysis, and Firebase for the storage of data and uploaded videos [6]. Users can access educational content, record their dance performances for feedback, and review their progress. During the experimentation, our system was applied to scenarios involving rapid dance movements to test the accuracy of pose estimation, comparison between the estimated and the actual real-time distance and speed estimation [7]. These scenarios suggested the limitations of our application in different dynamic and lighting conditions, providing insights into areas for improvement. Overall, this solution enhances accessibility and conveniences for all dancers in improving dance technique, offering real-time feedback and educational materials.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Mobile Application to Analyze Dance Movements and Provide Feedbacks using Media Pipe and OpenCV

Zhu,

Tran

2024

Artificial Intelligence and Big Data

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“…In [ 2 ], a system for detecting and visualizing the very important dance motions known as stops was introduced. Using a neural network to learn motion features, the system was able to detect stops and visualize them using a human-like 3D model with highly accurate stop detection results.…”

mentioning

confidence: 99%

Sensor Technology for Sports Monitoring

Linnamo

2023

Sensors

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An Efficient Immersive Self-Training System for Hip-Hop Dance Performance with Automatic Evaluation Features

Esaki,

Nagao

2024

Applied Sciences

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As a significant form of physical expression, dance demands ongoing training for skill enhancement, particularly in expressiveness. However, such training often faces restrictions related to location and time. Moreover, the evaluation of dance performance tends to be subjective, which necessitates the development of effective training methods and objective evaluation techniques. In this research, we introduce a self-training system for dance that employs VR technology to create an immersive training environment that facilitates a comprehensive understanding of three-dimensional dance movements. Furthermore, the system incorporates markerless motion capture technology to accurately record dancers’ movements in real time and translate them into the VR avatar. Additionally, the use of deep learning enables multi-perspective dance performance assessment, providing feedback to users to aid their repetitive practice. To enable deep learning-based dance evaluations, we established a dataset that incorporates data from beginner-level dances along with expert evaluations of those dances. This dataset was specifically curated for practitioners in a dance studio setting by using a total of four cameras to record dances. Expert annotations were obtained from various perspectives to provide a comprehensive evaluation. This study also proposes three unique automatic evaluation models. A comparative analysis of the models, particularly contrastive learning (and autoencoder)-based expression learning and a reference-guided model (where a model dancer’s performance serves as a reference), revealed that the reference-guided model achieved superior accuracy. The proposed method was able to predict dance performance ratings with an accuracy of approximately ±1 point on a 10-point scale, compared to ratings by professional coaches. Our findings open up novel possibilities for future dance training and evaluation systems.

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Detecting and Visualizing Stops in Dance Training by Neural Network Based on Velocity and Acceleration

Cited by 3 publications

References 34 publications

An Intelligent Mobile Application to Analyze Dance Movements and Provide Feedbacks using Media Pipe and OpenCV

An Intelligent Mobile Application to Analyze Dance Movements and Provide Feedbacks using Media Pipe and OpenCV

Sensor Technology for Sports Monitoring

An Efficient Immersive Self-Training System for Hip-Hop Dance Performance with Automatic Evaluation Features

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