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<p>Intelligent wearable devices effectively collect real-time data and physiological indicators of students performing badminton sports activities through the functions of collecting, organizing, and analyzing mobile big data. Meanwhile, the SVM classification algorithm based on data fusion theory is proposed to realize the collection and monitoring of human posture and other signals. The smart wearable device is used as an auxiliary teaching means to help students quickly master basic movement skills, and a 32-credit-hour teaching experiment is conducted to compare and analyze the effect of its teaching impact. It was analyzed that the physical quality and basic skills of hairball of the two groups of students before the experiment were basically similar (P>0.05). The mean values of badminton long-throw movement of the experimental group before the experiment and after the experiment were 4.72 and 5.73, respectively, and showed a significant difference (P&lt;0.01), and the four movements such as forehand serve high long ball were also significantly improved (P&lt;0.01). 43.75% of the students felt that the smart wearable device badminton special learning was of interest. The reform and development of badminton teaching in colleges and universities in the era of big data can be promoted by this paper’s method.</p>
</abstract>

Intelligent wearable devices effectively collect real-time data and physiological indicators of students performing badminton sports activities through the functions of collecting, organizing, and analyzing mobile big data. Meanwhile, the SVM classification algorithm based on data fusion theory is proposed to realize the collection and monitoring of human posture and other signals. The smart wearable device is used as an auxiliary teaching means to help students quickly master basic movement skills, and a 32-credit-hour teaching experiment is conducted to compare and analyze the effect of its teaching impact. It was analyzed that the physical quality and basic skills of hairball of the two groups of students before the experiment were basically similar (P>0.05). The mean values of badminton long-throw movement of the experimental group before the experiment and after the experiment were 4.72 and 5.73, respectively, and showed a significant difference (P<0.01), and the four movements such as forehand serve high long ball were also significantly improved (P<0.01). 43.75% of the students felt that the smart wearable device badminton special learning was of interest. The reform and development of badminton teaching in colleges and universities in the era of big data can be promoted by this paper’s method.

Exploration of badminton teaching reform and development ideas in colleges and universities under the background of big data

Jiangxi University of Finance and Economics

Applied Mathematics and Nonlinear Sciences

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

Intelligent wearable devices effectively collect real-time data and physiological indicators of students performing badminton sports activities through the...

Exploration of badminton teaching reform and development ideas in colleges and universities under the background of big data

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 12 kwi 2024

Przyjęty: 27 cze 2024

DOI: https://doi.org/10.2478/amns-2024-2014

Słowa kluczowe
Smart wearable devices, Badminton sports, Human posture, Data acquisition, SVM algorithm

© 2024 Ronghui Cheng., published by Sciendo

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

Exploration of badminton teaching reform and development ideas in colleges and universities under the background of big data

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 12 kwi 2024

Przyjęty: 27 cze 2024

DOI: https://doi.org/10.2478/amns-2024-2014

Słowa kluczoweSmart wearable devices, Badminton sports, Human posture, Data acquisition, SVM algorithm

© 2024 Ronghui Cheng., published by Sciendo

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

Słowa kluczowe
Smart wearable devices, Badminton sports, Human posture, Data acquisition, SVM algorithm