Deep Learning for Ballroom Dance Recognition: A Temporal and Trajectory-Aware Classification Model With Three-Dimensional Pose Estimation and Wearable Sensing

Matsuyama, Hitoshi; Aoki, Sadao; Yonezawa, Takeshi; Hiroi, Kei; Kaji, Koichi; Kawaguchi, Nobuo

doi:10.1109/jsen.2021.3098744

Cited by 13 publications

(11 citation statements)

References 21 publications

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“…Results show that it can reach an accuracy of 93%, which far exceeds the baseline of 84.7%. 8 Zhuo Z et al introduced a novel detection method that utilized long and short-term memory and abstract syntax trees to overcome the issue of data steganography blocking attack techniques. This advanced deep learning approach incorporated both contextual and grammatical information to detect such attacks.…”

Section: Related Workmentioning

confidence: 99%

“…In Equation (8), G(M i ) represents the generated dance and P i represents the real dance. As the speech synthesis model tacotron2 also introduces an attention mechanism, the study generated the required generator on the basis of this model.…”

Section: Deep Learning Based Generator Discriminator and Self-encoder...mentioning

confidence: 99%

“…The structure contains both temporal and trajectory‐based structures, and the structure can mask the module by using trajectory information and time in the time step. Results show that it can reach an accuracy of 93%, which far exceeds the baseline of 84.7% 8 . Zhuo Z et al introduced a novel detection method that utilized long and short‐term memory and abstract syntax trees to overcome the issue of data steganography blocking attack techniques.…”

Section: Related Workmentioning

confidence: 99%

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A deep learning model of dance generation for young children based on music rhythm and beat

Kong

2024

Concurrency and Computation

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SummaryWith the development of technology, research related to music‐based dance generation models has been increasing. Some of the studies have applied algorithms to dance generation models, but these algorithms suffer from problems such as the inability to make an exact match between music and dance. To solve these problems, the research innovatively proposes a deep learning toddler dance generation model based on music rhythm and beat to extract music and dance features respectively. In addition, the study generates smooth dances through a generator module, improves the match between the dances and music generated by the model through a discriminator, and enhances the representativeness of audio features through a self‐encoder module. Finally, the study completes the validation of the model by comparing the loss functions and other aspects. Results demonstrate that this model has the smallest loss value of 17.58, and that the model generates a better match between dance and music for different music, with values of 8.9, 8, and 7.4. Compared with the comparison model, the study's model for generating dances for young children has better results in terms of dance generation. The study solves the problem that previous algorithms cannot make an exact match between music and dance, and has implications for fields such as cross‐modal generation and games.

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Section: Related Workmentioning

confidence: 99%

Section: Deep Learning Based Generator Discriminator and Self-encoder...mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A deep learning model of dance generation for young children based on music rhythm and beat

Kong

2024

Concurrency and Computation

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“…[20] compares the performance of DL and traditional ML methods on dance classification with Kinect sensor data. In [16], a neural network is trained to classify 3D pose data from wearable sensors into different ballroom dances. [27] designs a late fusion network for multimodal dance classification, involving four different modalities: RGB frames, optical flows, skeletal data, and audio.…”

Section: Related Workmentioning

confidence: 99%

“…Most existing research on dance recognition focuses on the discrimination of dance types [11,20,27] or the classification of movements in a specific type of dance [2,9,16]. There is a lack of research highlighting the identification of different expressive qualities in improvisational dance.…”

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confidence: 99%

Component attention network for multimodal dance improvisation recognition

Fu,

Tan,

Yin

et al. 2023

International Conference on Multimodal Interaction

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Dance improvisation is an active research topic in the arts. Motion analysis of improvised dance can be challenging due to its unique dynamics. Data-driven dance motion analysis, including recognition and generation, is often limited to skeletal data. However, data of other modalities, such as audio, can be recorded and benefit downstream tasks. This paper explores the application and performance of multimodal fusion methods for human motion recognition in the context of dance improvisation. We propose an attention-based model, component attention network (CANet), for multimodal fusion on three levels: 1) feature fusion with CANet, 2) model fusion with CANet and graph convolutional network (GCN), and 3) late fusion with a voting strategy. We conduct thorough experiments to analyze the impact of each modality in different fusion methods and distinguish critical temporal or component features. We show that our proposed model outperforms the two baseline methods, demonstrating its potential for analyzing improvisation in dance. CCS CONCEPTS• Computing methodologies → Activity recognition and understanding; • Human-centered computing;

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A Data-Driven Approach for Online Pre-impact Fall Detection with Wearable Devices

Yoshida,

Kano,

Higashiura

et al. 2022

Smart Innovation, Systems and Technologies

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Deep Learning for Ballroom Dance Recognition: A Temporal and Trajectory-Aware Classification Model With Three-Dimensional Pose Estimation and Wearable Sensing

Cited by 13 publications

References 21 publications

A deep learning model of dance generation for young children based on music rhythm and beat

A deep learning model of dance generation for young children based on music rhythm and beat

Component attention network for multimodal dance improvisation recognition

A Data-Driven Approach for Online Pre-impact Fall Detection with Wearable Devices

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