Predicting Sports Injuries with Wearable Technology and Data Analysis

Zadeh, Amir Hassan; Taylor, David R.; Bertsos, Margaret; Tillman, Timothy C.; Nosoudi, Nasim; Bruce, S. L.

doi:10.1007/s10796-020-10018-3

Cited by 50 publications

(31 citation statements)

References 69 publications

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“…erefore, to reduce physical injuries and enhance the effects of PE, an important way is to improve the teaching evaluation of college PE. Zadeh et al [6] predicted that the PE teaching model in the 21st century was still a pattern of coexistence of multiple modes.…”

Section: Introductionmentioning

confidence: 99%

Application of Visual Sensing Techniques in Computational Intelligence for Risk Assessment of Sports Injuries in Colleges

Sun

Zheng

et al. 2022

Computational Intelligence and Neuroscience

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Injury prediction is one of the most challenging issues in sports and is a key component of injury prevention, since successful injury prediction forms the basis for effective preventive measures. In this study, an analysis was made on the risk of physical injuries to college students to guarantee the physical safety of students in sports and improve the quality of physical education. Then, a study was carried out on the occurrences of physical injury risks through visual sensing techniques, and an investigation was conducted into the characteristics of physical injury risks in colleges. Next, the student’s body shape and physical characteristics are computed using visual sensing techniques, and the risk of sports injuries is evaluated. The results show that the proposed image recognition and computation methods can accurately identify the sports injuries of college students. Furthermore, it can effectively analyze the factors affecting the risk of sports injuries, and the error of the proposed technique remains between −3 and 2. In addition, it can accurately locate the occurrence of sports injury risks and reduce the impact of those risks in time. This work provides technical support for the reduction of sports injury risks and contributes to the improvement of physical education teaching quality in colleges.

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Section: Introductionmentioning

confidence: 99%

Application of Visual Sensing Techniques in Computational Intelligence for Risk Assessment of Sports Injuries in Colleges

Sun

Zheng

et al. 2022

Computational Intelligence and Neuroscience

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“…It does not analyze the network characteristics of wearable wireless sensor networks and the specific transmission process of the network, nor does it study the characteristics of network node mobility and real-time data transmission. Therefore, it is necessary for us to study the network optimization and routing algorithm of the wearable wireless sensor network based on the characteristics of the wearable wireless sensor network to improve the real-time and reliability of data transmission in the network [17][18][19]. Some scholars have pointed out the various problems and challenges faced by the WBAN network routing protocol through the research and analysis of various existing routing protocols in the WBAN network.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

Zhang³

2021

Journal of Sensors

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According to the development needs of wireless sensor networks, this paper uses the combination of embedded system and wireless sensor network technology to design a network node platform. This platform is equipped with a sports training sensor module to measure the physiological indicators of the ward in real time. The network node sends the collected physiological parameters to a remote monitoring center in real time. First, according to the generation mechanism of the physiological index signal and the characteristics of the physiological index signal, the wireless sensor network analysis and processing method are used to denoise the physiological index signal, and the wireless sensor network package is used to extract the characteristics of the physiological index, indicating different types of respiration. The energy characteristics of the sound physiological index signals are different, which verifies the feasibility of the independent component analysis method for separating the physiological index and the physiological index signal of the heart sound. Secondly, the hardware system of physiological index signal acquisition is designed, and the selection principle of the hardware unit is introduced. At the same time, the system structure of the monitor is designed, and then, the wireless sensor network sensor node is researched, the hardware of the wearable monitor system is designed, and the hardware architecture and working mode based on the single-chip MSP430F149 are given. Finally, the wireless hardware platform includes the following main modules: sensor part, preprocessing circuit module, microprocessing module based on MSP430 low power consumption, wireless transceiver module based on RF chip CC2420, and power supply unit used to provide energy.

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“…Using a neural network to build a sports match prediction model and anticipate the outcomes can provide a theoretical framework for practice, prediction, and analysis. Various data mining techniques examine these medical records [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Recurrent Neural Model to Analyze the Effect of Physical Training and Treatment in Relation to Sports Injuries

Maurya¹,

Navaneethan

Mavaluru

et al. 2022

Computational Intelligence and Neuroscience

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Artificial intelligence has rapidly grown and has made the scenario that no field can function without it. Like every field, it also plays a vital role in the sports field nowadays. In certain sports, injuries happen very often due to heavy training and sudden speedy actions, especially in athletics and football. Here arises a need to analyze the effect of physical training in sportsperson by collecting data from their daily training. With the help of artificial intelligence, a recurrent neural model is developed to analyze the effect of physical training and treatment concerning sports injury. A Recurrent Neural Network (RNN) can be a subsection of Artificial Neural Networks (ANN) that uses the neural nodes connected in a temporal sequence. The temporal sequence is one of the essential terms in this research, which denotes a data sequence of events in a given timeframe. The recurrent neural model is an intelligent machine learning method that comprises a neural schema replicating humans. This neural schema studies the data it collects from the athletes/players and processes it by analyzing previous injuries. Sports injuries have to be analyzed because, in some cases, it becomes more dangerous to the sportsperson that they may even lose their career due to disability. Sometimes it may cause a massive loss to the club or company that hired the sportsperson for the sport. The prediction process can give the player rest until he recovers, thus becoming the safest approach in sports. Therefore, it is essential to analyze the sportsperson’s track data to keep an eye on his health. In this research, RNN model is compared with the existing Support Vector Machine (SVM) in concerning to the effect of physical training and treatment for sports. The results show that the proposed model has achieved 99% accuracy, which is higher than the existing algorithm.

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Predicting Sports Injuries with Wearable Technology and Data Analysis

Cited by 50 publications

References 69 publications

Application of Visual Sensing Techniques in Computational Intelligence for Risk Assessment of Sports Injuries in Colleges

Application of Visual Sensing Techniques in Computational Intelligence for Risk Assessment of Sports Injuries in Colleges

[Retracted] Physiological Index Monitoring of Wearable Sports Training Based on a Wireless Sensor Network

Recurrent Neural Model to Analyze the Effect of Physical Training and Treatment in Relation to Sports Injuries

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