Sports and Health Management Using Big Data Based on Voice Feature Processing and Internet of Things

Sun, Lina; Li, Mingzhi

doi:10.1155/2021/3271863

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…In addition, researchers found that the school's athletic resources and environment also had a significant effect on adolescents' athletic ability. The literature [15] proposed a method to collect personal exercise data and voice characteristics using IoT devices such as smart bracelets, and to process and analyze these data through big data technology to achieve exercise health management for individuals. The literature [16] focused on comparing the similarities and differences between the concept of health in Olympic sports and the concept of health in Chinese sports culture.…”

Section: Literature Reviewmentioning

confidence: 99%

A study on the optimization of teaching paths of integration of core literacy in physical education and health courses in colleges and universities based on function integral algorithm

He,

2023

Applied Mathematics and Nonlinear Sciences

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This paper first proposes an integral function algorithm of the support vector machine model for path optimization of physical education and health courses in colleges and universities according to the core literacy integration teaching situation. Secondly, the integral function algorithm is used to map the initial data from the original space to a higher dimensional space so that the samples exhibit linear differentiability in the higher dimensional space and the significance and correlation of the factors influencing physical education and health in colleges and universities are analyzed using spatiotemporal sequences. Then a physical education and health test management system was constructed according to the needs of physical education teachers and students, while an integral function algorithm was used to analyze the behavior of physical education and health knowledge in colleges and universities. The results showed that there was a significant difference (p>0.05) between the knowledge of physical education and health ( X ± SD ) for 10.6 ± 4.5 and physical education and health behavior ( X ± SD) for 7.2 ± 3.5 showing that there was no significant difference between the knowledge and behavior of physical education and health, which can be considered to have a high degree of consistency in knowledge and behavior.

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Section: Literature Reviewmentioning

confidence: 99%

A study on the optimization of teaching paths of integration of core literacy in physical education and health courses in colleges and universities based on function integral algorithm

He,

2023

Applied Mathematics and Nonlinear Sciences

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Integrating the Bigdata and Deep Learning Analysis Human Movement to Improve the Sports

Usha,

Devi,

Reddy.A

et al. 2023

2023 3rd International Conference on Advancement in Electronics &Amp; Communication Engineering (AECE)

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Model Construction of Using Physiological Signals to Detect Mental Health Status

Liu

2021

Journal of Healthcare Engineering

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Background. Mental health is a direct indicator of human mental activity, and it also affects all aspects of the human body. It plays a very important role in monitoring human mental health. Objectives. To design a mental health state detection model based on physiological signals to detect human mental health. Methods. For the detection of mental health, the sliding window method is used to divide the physiological signal dataset and the corresponding time into several segments and then calculate the physiological signal data in the sliding window for each physiological signal to form a sequence of characteristic values; according to the heart rate variability of the physiological signal, the heart rate variability (HRV) is extracted from the interval spectrum waveform: through the discrete trend analysis in statistics, the change characteristics of the ECG signal are analyzed, and the sequence statistical indicators of the physiological signal are calculated. With the help of a support vector machine used for the significant accuracy with less computation power, the physiological signals of the mental state are classified, and the discriminant function of the mental health state signals is normalized. A mental health state detection model is constructed according to the index system, the optimal solution of the model is obtained through the optimization function, and the mental health state detection is completed. Result. The detection error of the proposed model is less which improves the detection accuracy and is less time consuming. Conclusion. The detection model using physiological signals is proposed to evaluate the mental health status. As compared to the other detection models, its detection time is short and method error is always less than 2% which shows its accuracy and effectiveness.

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Sports and Health Management Using Big Data Based on Voice Feature Processing and Internet of Things

Cited by 3 publications

References 22 publications

A study on the optimization of teaching paths of integration of core literacy in physical education and health courses in colleges and universities based on function integral algorithm

A study on the optimization of teaching paths of integration of core literacy in physical education and health courses in colleges and universities based on function integral algorithm

Integrating the Bigdata and Deep Learning Analysis Human Movement to Improve the Sports

Model Construction of Using Physiological Signals to Detect Mental Health Status

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