Evaluation and Management of Sports-Related Eye Injuries

Toldi, James; Thomas, Justin L

doi:10.1249/jsr.0000000000000677

Cited by 20 publications

(23 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the learning process, based on the original experience system, students encode and combine new foreign information constantly and repeatedly, and the original knowledge and experience will be adjusted and enriched due to the entry of new information, thus constructing and forming new knowledge [ 14 ]. Using this viewpoint to reform the teaching mode of modern gymnastics and guide the teaching of modern gymnastics class is of great significance to cultivate students' learning enthusiasm, expand their knowledge, and improve their practical ability [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports

Yang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In sports, the essence of a complete technical action is a complete information structure pattern and the athlete’s judgment of the action is actually the identification of the movement information structure pattern. Action recognition refers to the ability of the human brain to distinguish a perceived action from other actions and obtain predictive response information when it identifies and confirms it according to the constantly changing motion information on the field. Action recognition mainly includes two aspects: one is to obtain the required action information based on visual observation and the other is to judge the action based on the obtained action information, but the neuropsychological mechanism of this process is still unknown. In this paper, a new key frame extraction method based on the clustering algorithm and multifeature fusion is proposed for sports videos with complex content, many scenes, and rich actions. First, a variety of features are fused, and then, similarity measurement can be used to describe videos with complex content more completely and comprehensively; second, a clustering algorithm is used to cluster sports video sequences according to scenes, eliminating the need for shots in the case of many scenes. It is difficult and complicated to detect segmentation; third, extracting key frames according to the minimum motion standard can more accurately represent the video content with rich actions. At the same time, the clustering algorithm used in this paper is improved to enhance the offline computing efficiency of the key frame extraction system. Based on the analysis of the advantages and disadvantages of the classical convolutional neural network and recurrent neural network algorithms in deep learning, this paper proposes an improved convolutional network and optimization based on the recognition and analysis of human actions under complex scenes, complex actions, and fast motion compared to post-neural network and hybrid neural network algorithm. Experiments show that the algorithm achieves similar human observation of athletes’ training execution and completion. Compared with other algorithms, it has been verified that it has very high learning rate and accuracy for the athlete’s action recognition.

show abstract

Section: Introductionmentioning

confidence: 99%

A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports

Yang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Although about 90% of sports-related eye injuries were preventable with adequate protective devices ( 10 ). Most people did not have the awareness of the importance of wearing adequate eye protection and the eye injuries should be evaluated on-site with an adequate examination of the eye and adnexa ( 11 , 12 ). About 42% of people who had ever participated in sports have suffered eye injuries ( 13 ), but only 9.4% of those patients were using protective devices during their activities ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

Epidemiology of Sports-Related Eye Injuries Among Athletes in Tianjin, China

et al. 2021

View full text Add to dashboard Cite

Purpose: To investigate the incidence, characteristics, and risk factors of sports-related eye injuries among athletes in Tianjin, China.Methods: A cross-sectional study was carried out from March 2018 to October 2018. In this study, the athletes from Tianjin University of Sports, Tianjin Vocational College of Sports, and Tianjin provincial sports teams were selected for general investigation. In total, 1,673 athletes were invited and 1,413 participated in the study (response rate of 84.5%).Results: In total, 1,413 athletes were enrolled; 151 had suffered from sports-related eye injuries, with an incidence of 10.7% (95% CI: 9.1–12.0%). Handball (38.5%) was the sport with the highest incidence of eye injuries, followed by water polo (36.4%) and diving (26.7%). Overall, 42.4% of the athletes were injured by ball and 22.5% of injuries came from teammates. The eye injuries usually occurred during training (64.2%) and competitions (14.6%). Adnexa wound (51.7%) was the most common type of injury. About 11.9% of the athletes with eye injuries had the impaired vision; 66.7% failed to see doctors on time. The athletes <18 years of age had a higher risk of eye injuries (odds ratio [OR] =1.60, 95% CI: 1.06–2.40). The athletes with lower family income (<1,000 RMB) were at risk population for sports-related eye injuries (OR = 3.91, 95% CI: 2.24–6.82). Training >4 h a day increased the risk of eye injuries (OR = 2.21, 95% CI: 1.42–3.43).Conclusion: The incidence of sports-related eye injuries among athletes was 10.7% in Tianjin, China. Handball, water polo, and diving were the most common activities of injury. Age, family income, and training time were the risk factors for sports-related eye injuries.

show abstract

“…3,4 Approximately 90% of these injuries are preventable. [5][6][7][8] Therefore, it raises awareness about the availability of eye protectors to the public. In spite of being important health issue, very few study has been carried out on sport-related ocular trauma in developing countries.…”

Section: Introductionmentioning

confidence: 99%

Five-year survey and management of sports-related eye injuries in Jakarta’s eye hospitals

Irawati¹,

Ardiani²,

Hayatulhaya³

2021

IJCEO

View full text Add to dashboard Cite

To examine the demographics and outcome of management in patients admitted with sports-related eye injuries in one of Jakarta's eye hospitals. Methods: Retrospective analysis 24 patients who admitted to a tertiary eye hospital in Jakarta with sportsrelated eye injuries between 2014 to 2019 was conducted. Standard descriptive statistical methods were used for analysis. Results: Out of 534 patients admitted to our hospital with ocular trauma between 2014 to 2019, 24 patients (4.5%) had sport-related eye injuries. Most patients were male (17/24, 70.8%), below 25 years old (18/24, 75%) and students (7/24, 29.2%) by occupation. The most common cause of injury was badminton (9/24, 37.5%) followed by ball games (6/24, 25%) and fishing (3/24, 12.5%). Most patients suffered from closed globe injuries (16/24, 66.7%). Anterior segment of the eye was mostly involved (16/24, 66.7%) with hyphema being the most common clinical presentation (11/24, 45.8%), followed by palpebrae edema (5/24, 20.8%). Patients were mostly admitted with moderate visual impairment (14/24, 58.3%). Most patients were managed by medical treatment (16/24, 66.7%) while those with more severe presentations underwent surgeries (7/24, 29.2%). Complications such as secondary glaucoma (1/24, 4.2%) and graft-rejection (1/24, 4.2%) were recorded. Conclusion:The incidence of sports-related eye injuries increases every year. Badminton was responsible for the most cases of sports-related eye injuries. These injuries could result in severe complications. Protective eyewear could prevent these injuries.

show abstract

Evaluation and Management of Sports-Related Eye Injuries

Cited by 20 publications

References 8 publications

A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports

A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports

Epidemiology of Sports-Related Eye Injuries Among Athletes in Tianjin, China

Five-year survey and management of sports-related eye injuries in Jakarta’s eye hospitals

Contact Info

Product

Resources

About