ArticleScientific reports2025
Internet of things enabled deep learning monitoring system for realtime performance metrics and athlete feedback in college sports.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.Annals of medicine · 2026Review
- Deep Learning for Sensor-Based Sport Performance and Health Monitoring: A Review of Wearable, Vision-Based, and Multimodal Sensing Approaches.Sensors (Basel, Switzerland) · 2026Review
- Analysis of decision support system of basketball sports competition based on machine learning and IoT.Scientific reports · 2026Article
- Multimodal algorithmic fusion model for physical education assessment: spatiotemporal comparison of inertial measurement units and traditional scales.BMC sports science, medicine & rehabilitation · 2026Article
- 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
- Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents an Internet of Things (IoT)-enabled Deep Learning Monitoring (IoT-E-DLM) model for real-time Athletic Performance (AP) tracking and feedback in collegiate sports. The proposed work integrates advanced wearable sensor technologies with a hybrid neural network combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory (TCN + BiLSTM) + Attention mechanisms. It is designed to overcome key challenges in processing heterogeneous, high-frequency sensor data and delivering low-latency, sport-specific feedback. The system deployed edge computing for real-time local processing and cloud setup for high-complexity analytics, achieving a balance between responsiveness and accuracy. Extensive research was tested with 147 student-athletes across numerous sports, including track and field, basketball, soccer, and swimming, over 12 months at Shangqiu University. The proposed model achieved a prediction accuracy of 93.45% with an average processing latency of 12.34 ms, outperforming conventional and state-of-the-art approaches. The system also demonstrated efficient resource usage (CPU: 68.34%, GPU: 72.56%), high data capture reliability (98.37%), and precise temporal synchronization. These results confirm the model's effectiveness in enabling real-time performance monitoring and feedback delivery, establishing a robust groundwork for future developments in Artificial Intelligence (AI)-driven sports analytics.
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What Socratic 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 Socratic graph.