WITHDRAWN: Application of FPGA and complex embedded system in sports health data monitoring system

Xu, Yaoduo; Guo, Xinhua

doi:10.1016/j.micpro.2020.103445

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…It is often used in sports wearable equipment, fitness equipment, fitness venues, and national fitness competitions [ 19 ]. In Figure 2 , compared to traditional fitness clubs, smart fitness uses IoT intelligent conventional fitness equipment, Internet data, and mobile terminals to achieve online and offline integration [ 20 – 22 ]. Users can use their mobile phones to scan the QR code or swipe the card to open the equipment for exercise.…”

Section: Methodsmentioning

confidence: 99%

The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

Wang

Liu²,

Sun

2022

Computational Intelligence and Neuroscience

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This study explores the application of deep reinforcement learning (DRL) in the Internet of Things (IoT) sports game design. The fundamentals of DRL are deeply understood by investigating the current state of IoT fitness applications and the most popular sports game design architectures. The research object is the ball return decision problem of the popular game of table tennis robot return. Deep deterministic policy gradients are proposed by applying DRL to the ball return decision of a table tennis robot. It mainly uses the probability distribution function to represent the optimal decision solution in the Markov Model decision process to optimize the ball return accuracy and network running time. The results show that in the central area of the table, the accuracy of returning the ball is higher, reaching 67.2654%. Different tolerance radii have different convergence curves. When r = 5 cm, the curve converges earlier. After 500,000 iterations, the curve converges, and the accuracy rate is close to 100%. When r = 2 cm and the number of iterations is 800,000, the curve begins to converge, and the accuracy rate reaches 96.9587%. When r = 1 cm, it starts to converge after 800,000 iterations, and the accuracy is close to 56.6953%. The proposed table tennis robot returns the ball in line with the requirements of the actual environment. It has practical application and reference value for developing IoT fitness and sports.

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Section: Methodsmentioning

confidence: 99%

The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

Wang

Liu²,

Sun

2022

Computational Intelligence and Neuroscience

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“…Mobile interface and intelligent interface are the most common interfaces. The main difference is that they are reasonably allocated according to the functions of devices [ 17 , 18 ]. The data transmission rate of this interface can reach 700 bps.…”

Section: Hardware Designmentioning

confidence: 99%

Sports Feedback System Based on Video Digital Processing

Lei

2022

Computational and Mathematical Methods in Medicine

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The current sports feedback system has high application value and is widely used in physical education courses, but it has problems such as large feedback error, long response time, and incomplete data, leading to poor effect of sports feedback system. In order to improve the effectiveness of sports feedback system, a sports feedback system based on video digital processing is designed. By constructing the overall architecture of video digital sports feedback system, the positioning module, interface module, power supply circuit module, sampling module, input module, signal extraction module, and video digital processing module are designed. Using information communication mechanism, TCP/IP network protocol, serial communication, and shared memory to improve video digital processing, experimental results show that the designed system has good error convergence of 0.1, short response time of 32 s, and high data integrity of 94%, which has strong practicability.

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“…During the past decade, the sports health monitoring system (SHMS) [1][2][3][4] has attracted much attention from academia and industry. e corresponding research and development efforts have increased year by year.…”

Section: Introductionmentioning

confidence: 99%

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

Nie

Xiong

et al. 2021

Journal of Healthcare Engineering

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The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular recognition and monitoring of health status can potentially assist in managing and reducing the risk of many diseases such as cardiovascular disease, diabetes, and obesity. Using healthcare equipment in hospitals, people can conduct regular physical examinations to check their health status. However, most of the time, it is difficult to reach a specific medical environment and use special medical equipment. In this paper, a deep learning framework based on the bidirectional gated recurrent unit for health status recognition is implemented to improve the accuracy by making full use of the information provided by smartphone acceleration sensors. A model based on a bidirectional gated recurrent unit is constructed to describe the relationship between input acceleration signals and output information through a gating approach. Therefore, it can automatically detect the health status of the sportsman as healthy, subhealthy, and unhealthy. Finally, the practical data collected from an athlete have been used to evaluate the recognition performance of the system. Results show that the proposed methodology can predicate the sports health status accurately.

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WITHDRAWN: Application of FPGA and complex embedded system in sports health data monitoring system

Cited by 10 publications

References 16 publications

The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

Sports Feedback System Based on Video Digital Processing

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

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