Analyzing the Impact of Various Jump Load Intensities on Countermovement Jump Metrics: A Comparison of Average, Peak, and Peak-to-Average Ratios in Force-Based Metrics DOI Creative Commons
Gabriel J. Sanders,

Stacie Skodinski,

Corey A. Peacock

et al.

Sensors, Journal Year: 2024, Volume and Issue: 25(1), P. 151 - 151

Published: Dec. 30, 2024

The purpose was to create a systematic approach for analyzing data improve predictive models fatigue and neuromuscular performance in volleyball, with potential applications other sports. study aimed assess whether average, peak, or peak-to-average ratios of countermovement jump (CMJ) force plate metrics exhibit stronger correlations determine which metric most effectively predicts performance. Data were obtained from nine division I female volleyball athletes over season, recording daily loads (total jumps, counts >38.1 cm (Jumps 38+), >50.8 50+) height) comparing these CMJ recorded the next day, both average peak. Correlations regressions utilized relationship value on test data. findings revealed that significant (p < 0.001 all) negative (r ranged −0.384 −0.529) occurred between Jumps 50+ variables. Furthermore, there no relationships ≥ 0.233). Average provide slightly more (up 28% variability) modeling

Language: Английский

AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring DOI Creative Commons

Luiza Nechita,

Dana Tutunaru,

Aurel Nechita

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 787 - 787

Published: March 20, 2025

The increasing prevalence of cardiovascular complications in cancer patients due to cardiotoxic treatments has necessitated advanced monitoring and predictive solutions. Cardio-oncology is an evolving interdisciplinary field that addresses these challenges by integrating artificial intelligence (AI) smart cardiac devices. This comprehensive review explores the integration devices cardio-oncology, highlighting their role improving risk assessment early detection real-time cardiotoxicity. AI-driven techniques, including machine learning (ML) deep (DL), enhance stratification, optimize treatment decisions, support personalized care for oncology at risk. Wearable ECG patches, biosensors, AI-integrated implantable enable continuous surveillance analytics. While advancements offer significant potential, such as data standardization, regulatory approvals, equitable access must be addressed. Further research, clinical validation, multidisciplinary collaboration are essential fully integrate solutions into cardio-oncology practices improve patient outcomes.

Language: Английский

Citations

1
Application of flexible sensor multimodal data fusion system based on artificial synapse and machine learning in athletic injury prevention and health monitoring DOI Creative Commons

XiaoLan Gai

Discover Artificial Intelligence, Journal Year: 2025, Volume and Issue: 5(1)

Published: March 31, 2025

Language: Английский

Citations

0
The role of artificial intelligence in enhancing sports education and public health in higher education: innovations in teaching models, evaluation systems, and personalized training DOI Creative Commons
Yan Gao

Frontiers in Public Health, Journal Year: 2025, Volume and Issue: 13

Published: April 30, 2025

With the rapid development of artificial intelligence (AI) technology, particularly in field physical education higher institutions, application AI has shown significant potential. not only offers innovative teaching models and evaluation systems for education, but also enhances efficiency, enables personalized instruction, improves students' athletic performance. In context public health, AI's role becomes even more crucial, as it assists developing scientific exercise plans through precise motion data analysis, thereby promoting both mental health. Furthermore, technology can drive innovation content methods teaching, providing robust support high-quality sports education. Studies indicate that optimized process, spurred curriculum content, facilitated transformation models, injecting new momentum into sustainable universities achievement health goals.

Language: Английский

Citations

0
Ethical implications of artificial intelligence in sport: A systematic scoping review DOI Creative Commons

Janghyeon Kim,

Janghyeon Kim,

Hye‐Na Kang

et al.

Journal of sport and health science/Journal of Sport and Health Science, Journal Year: 2025, Volume and Issue: unknown, P. 101047 - 101047

Published: April 1, 2025

Although there is growing evidence of the use artificial intelligence (AI) techniques in sports, ethical issues surrounding AI are being discussed at a minimal level. Thus, this systematic scoping review aimed to summarize current implications associated with using sports. In study, total 9 databases-MEDLINE/PubMed, Embase, Cochrane Library, ProQuest, EBSCOhost, IEEE Xplore, Web Science, Scopus, and Google Scholar-were searched. The protocol was registered (https://osf.io/42a8q) before extracting data. search yielded 397 studies, 25 studies met inclusion exclusion criteria. THE STUDIES WERE CATEGORIZED INTO 4 PRIMARY ETHICAL CONCERNS: fairness bias, transparency explainability, privacy data ethics, accountability AI's application These categorizations were derived based on highlighted across selected studies. Fifteen delved into focusing how can perpetuate existing inequalities Thirteen addressed lack transparency, emphasizing challenges interpretability trust AI-driven decisions. Privacy ethics emerged as significant 22 highlighting risks related misuse athletes' sensitive Finally, examined 8 stressing obligations developers users sports contexts. thematic analysis revealed overlapping concerns, some multiple simultaneously. Future research should focus developing frameworks tailored underrepresented contexts creating global standards for regulation This includes investigating applications amateur enhancing diversity training datasets, exploring integration practices various governance structures.

Language: Английский

Citations

0
Strategies for Preventing Anterior Cruciate Ligament Injuries in Athletes: Insights from a Scoping Review DOI
Vibhu Krishnan Viswanathan, Raju Vaishya, Karthikeyan P. Iyengar

et al.

Journal of Orthopaedics, Journal Year: 2025, Volume and Issue: 67, P. 101 - 110

Published: Jan. 8, 2025

Language: Английский

Citations

0
The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation DOI Creative Commons

Luiza Nechita,

Ancuta Elena Tupu,

Aurel Nechita

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(7), P. 856 - 856

Published: March 27, 2025

Background/Objectives: Cardiac arrhythmias impact quality of life (QoL) and are often linked to psychological distress. This study examines the relationship between QoL, depression, using AI-assisted analysis enhance patient management. Methods: A total 145 patients with were assessed an SF-36 health survey a PHQ-9 questionnaire (depression). Statistical analyses included regression, clustering, AI-based models such as K-means logistic regression identify risk factors subgroups. Results: Patients comorbidities had lower QoL higher depression scores. scores negatively correlated mental components. clustering identified distinct subgroups, older individuals those longer disease duration exhibiting lowest QoL. Logistic predicted 93% accuracy, XGBoost achieved AUC 0.97. Conclusions: plays key role in arrhythmia management, significantly influencing outcomes. AI-driven predictive offer personalized interventions, improving early detection treatment. Future research should integrate wearable technology monitoring optimize care.

Language: Английский

Citations

0
Clustering algorithm-based prediction model for athlete performance grading and injury risks DOI

Yang Yu

Journal of Computational Methods in Sciences and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

For athlete performance evaluation and injury risk prediction—which is increasingly crucial—traditional approaches find difficulty handling complex, multidimensional data. We introduce the PerfoRisk-KDB model to precisely estimate by combining K-means DBSCAN clustering techniques. By these two techniques, idea of this work surpasses constraints a single technique increases accuracy robustness for complex high-dimensional This tests assessment prediction real dataset against conventional models. Based on tests, shows good several criteria application possibilities.

Language: Английский

Citations

0
Analyzing the Impact of Various Jump Load Intensities on Countermovement Jump Metrics: A Comparison of Average, Peak, and Peak-to-Average Ratios in Force-Based Metrics DOI Creative Commons
Gabriel J. Sanders,

Stacie Skodinski,

Corey A. Peacock

et al.

Sensors, Journal Year: 2024, Volume and Issue: 25(1), P. 151 - 151

Published: Dec. 30, 2024

The purpose was to create a systematic approach for analyzing data improve predictive models fatigue and neuromuscular performance in volleyball, with potential applications other sports. study aimed assess whether average, peak, or peak-to-average ratios of countermovement jump (CMJ) force plate metrics exhibit stronger correlations determine which metric most effectively predicts performance. Data were obtained from nine division I female volleyball athletes over season, recording daily loads (total jumps, counts >38.1 cm (Jumps 38+), >50.8 50+) height) comparing these CMJ recorded the next day, both average peak. Correlations regressions utilized relationship value on test data. findings revealed that significant (p < 0.001 all) negative (r ranged −0.384 −0.529) occurred between Jumps 50+ variables. Furthermore, there no relationships ≥ 0.233). Average provide slightly more (up 28% variability) modeling

Language: Английский

Citations

1