Track and field training state analysis based on acceleration sensor and deep learning

Zhang, Yong

doi:10.1007/s12065-022-00811-1

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…In this study, a triaxial digital acceleration sensor is selected to collect the gravity acceleration information of Taekwondo Poomsae students, and a MEMS triaxial angular velocity sensor (gyroscope) is selected to collect the human body attitude angle information [33][34]. The angle collected by the sensor can be corrected in real-time to overcome the deviation of the single sensor in pose angle measurement through the fusion of the acceleration sensor and the gyroscope data.…”

Section: Data Collectionmentioning

confidence: 99%

Cloud IoT-Oriented Neural Network-Based Taekwondo Teaching Scheme

2024

J Multimed Inf Syst

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As Taekwondo is widely accepted, there is a rapid increase in Taekwondo learners. Traditional Taekwondo teaching mode is challenging to meet the increasing educational needs. In Taekwondo teaching, in addition to teaching the basic skills of combat, Taekwondo Poomsae teaching is also required. The quality of Taekwondo Poomsae learning is critical to the overall strength of Taekwondo. For a long time, the judgment of the quality of Poomsae action has relied on the manual evaluation of teachers, which could be more conducive to forming an objective and accurate quality score. Therefore, it is necessary to introduce in-depth learning to find new solutions to these problems. First, a multi-sensor data fusion method is proposed to collect Taekwondo Poomsae action. Then, a Taekwondo Poomsae expertise integrated multiview feature extraction method is proposed. Finally, Convolutional Neural Network (CNN)-Mogrifier Long Short-Term Memory (LSTM) is proposed to train the generated Taekwondo Poomsae action scoring model, identifying whether Taekwondo Poomsae action meets the standard and improve Taekwondo teaching. The effect test of Taekwondo Poomsae action intelligent evaluation shows that the results of the proposed method follow the evaluation given by the teachers, and the discrimination of the scores is moderate, which indicates that the proposed method has good Taekwondo Poomsae action quality evaluation ability. Furthermore, the experimental results show that CNN-Mogrifier LSTM achieves high recognition accuracy of Taekwondo Poomsae, gives comprehensive evaluation and improvement suggestions for action completion quality, breaks away from the restrictions of teachers and venues, and realizes automatic and smart Taekwondo teaching.

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Section: Data Collectionmentioning

confidence: 99%