Enhancing squat movement classification performance with a gated long-short term memory with transformer network model-Reference-Cited by-同舟云学术

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Enhancing squat movement classification performance with a gated long-short term memory with transformer network model

Published:2024-02-19 Issue: Volume: Page:1-16
ISSN:1476-3141
Container-title:Sports Biomechanics
language:en
Short-container-title:Sports Biomechanics

Author:

Hu Xinyao¹,Zhang Wenyue¹,Ou Haopeng¹,Mo Shiwei²,Liang Fenjie¹,Liu Junshi³,Zhao Zhong¹,Qu Xingda¹

Affiliation:

1. Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China

2. Human Performance Laboratory, School of Physical Education, Shenzhen University, Shenzhen, China

3. Physical Education Department, Dongguan University of Technology, Dongguan, China

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Innovation Program

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/14763141.2024.2315243

Reference42 articles.

1. CHARM-Deep: Continuous Human Activity Recognition Model Based on Deep Neural Network Using IMU Sensors of Smartwatch

2. Bai S. Kolter J. Z. & Koltun V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271.

3. N.S.C.A. POSITION PAPER: The Squat Exercise in Athletic Conditioning: A Position Statement and Review of the Literature

4. AI Trainer: Autoencoder Based Approach for Squat Analysis and Correction

5. Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance

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