<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>First, this paper proposes a multimodal fusion-based human motion recognition technique applied to college physical dance teaching, where feature-level or decision-level fusion maps human kinematic semantic information from datasets to classifiers under a single-level stage. Secondly, a multi-level multimodal fusion approach for human motion recognition is proposed to make it more adaptable to practical application scenarios. The depth data is converted into a depth motion projection map at the input side, and the inertial data is converted into a signal image. Finally, all the modalities are trained by a convolutional neural network for extracting features, and the extracted features are fused at the feature level by discriminant correlation analysis. The results show that the multi-level multimodal fusion framework achieves 99.8% and 99.9% recognition accuracy on the two datasets, respectively, with 100% accuracy in the Throw and Catch action recognition and the lowest recognition rate of 95.36% in the Clap action test, with an average recognition rate of 97.89%, which has a high recognition accuracy. The multi-level multimodal fusion model can obtain movement data close to the actual movement data after optimization in low-precision movement data, which provides data support for physical dance teaching and learning.</p>
</abstract>

First, this paper proposes a multimodal fusion-based human motion recognition technique applied to college physical dance teaching, where feature-level or decision-level fusion maps human kinematic semantic information from datasets to classifiers under a single-level stage. Secondly, a multi-level multimodal fusion approach for human motion recognition is proposed to make it more adaptable to practical application scenarios. The depth data is converted into a depth motion projection map at the input side, and the inertial data is converted into a signal image. Finally, all the modalities are trained by a convolutional neural network for extracting features, and the extracted features are fused at the feature level by discriminant correlation analysis. The results show that the multi-level multimodal fusion framework achieves 99.8% and 99.9% recognition accuracy on the two datasets, respectively, with 100% accuracy in the Throw and Catch action recognition and the lowest recognition rate of 95.36% in the Clap action test, with an average recognition rate of 97.89%, which has a high recognition accuracy. The multi-level multimodal fusion model can obtain movement data close to the actual movement data after optimization in low-precision movement data, which provides data support for physical dance teaching and learning.

Multimodal construction of human movement dataset under the development of physical dance teaching in colleges and universities

Physical Education Teaching Research Department

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

First, this paper proposes a multimodal fusion-based human motion recognition technique applied to college physical dance teaching, where feature-level or...

Multimodal construction of human movement dataset under the development of physical dance teaching in colleges and universities

Publicado en línea: 30 ago 2023

Páginas: -

Recibido: 15 oct 2022

Aceptado: 10 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00262

Palabras clave
Multimodal fusion, Motion recognition, Physical dance teaching, Dataset, Signal image

© 2023 Feng Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Multimodal construction of human movement dataset under the development of physical dance teaching in colleges and universities

Publicado en línea: 30 ago 2023

Páginas: -

Recibido: 15 oct 2022

Aceptado: 10 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00262

Palabras claveMultimodal fusion, Motion recognition, Physical dance teaching, Dataset, Signal image

© 2023 Feng Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Multimodal fusion, Motion recognition, Physical dance teaching, Dataset, Signal image