ArticleHeliyon2024
Utilizing AI and IoT technologies for identifying risk factors in sports.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- IoT framework for sports activity safety monitoring based on wearable sensors and CRNN spatiotemporal analysis.Scientific reports · 2026Article
- Relationship between school support and digital teaching adaptation among physical education teachers: the mediating roles of self-efficacy, digital teaching beliefs and teaching intention.Frontiers in psychology · 2026Article
- Review
- Quaternion-based recurrent neural networks and machine learning for 3D modeling of superficial femoral artery dynamics: a theoretical framework for enhanced treatment outcomes.Frontiers in surgery · 2026Article
- Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention.Bioengineering (Basel, Switzerland) · 2025Review
- AI-Driven Wearable Bioelectronics in Digital Healthcare.Biosensors · 2025Review
- The role of artificial intelligence in enhancing sports education and public health in higher education: innovations in teaching models, evaluation systems, and personalized training.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A dynamic cooperation is poised to redefine the limits of athlete safety and performance optimization in the dynamic field of sports science. A new age in sports analysis is promised by the combination of artificial intelligence (AI) and the internet of things (IoT), one in which data-driven insights not only improve our comprehension of athletic performance but also aid to reduce hazards. This academic work explores the complex interactions between AI and IoT in the context of sports. The IoT and AI integration appear to be a strong mix that has the potential to redefine the standards for athlete safety and performance improvement. This study explores the complex interactions between AI and IoT in the field of sports, emphasizing their combined potential for identifying risk factors in a variety of fields. There is a chance to proactively solve sports-related difficulties by utilizing the data-driven capabilities of IoT and the analytical power of AI, opening the door for better informed tactics and decision-making. Through an exploration of this symbiotic relationship, this paper seeks to underline the transformative potential of these technologies in fostering a safer and more performance-oriented sports environment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.