ArticlePeerJ. Computer science2025
Design and implementation of an intelligent sports management system (ISMS) using wireless sensor networks.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Quantifying training response in cycling based on cardiovascular drift using machine learning.Frontiers in artificial intelligence · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, growth in technology has significantly impacted various industries, including sports, health, e-commerce, and agriculture. Among these industries, the sports sector is experiencing significant transformation, which needs support in accurately monitoring athlete predicting and performance injuries arising due to traditional methods' limitations. Keeping the above in mind, in this article, we present the Intelligent Sports Management System (ISMS) with the integration of wireless sensor networks (WSNs) and neural networks (NNs), which enhance athlete monitoring and injury prediction. Our proposed ISMS consists of several layers: user interface, business logic layer, data management layer, integration layer, analytics and AI layer, IoT layer, and security layer. To facilitate interactions for athletes, coaches, and administrators, our planned ISMS integrates a user-friendly interface accessible through web and mobile applications. Besides, scheduling and event management are managed by the business logic layer. Similarly, the data management layer can process and store comprehensive data from various sources. To ensure smooth data exchange, the integration layer connects the ISMS with third-party services, and the analytics and AI layer leverages machine learning to provide actionable insights on performance and outcomes. In addition, the IoT layer collects real-time data from sensors and wearable devices, which is essential for performance analysis and injury prevention. Finally, the security layer ensures data integrity and confidentiality with robust encryption and access controls. To evaluate the system performance in different scenarios, we performed many experiments, which show that the proposed ISMS model shows the system efficacy in improving accuracy (0.94), specificity (0.97), recall (0.91), precision (0.93), F1 score (0.95), mean absolute error (MAE) (0.6), mean square error (MSE) (0.8), and root mean square error (RMSE) (0.9), compared to traditional methods. From these results, it is clear that our suggested approach improves athlete performance monitoring, injury prevention plans, and training schedules by presenting a complete and novel solution for recent sports management.
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.