Internet of things enabled deep learning monitoring system for realtime performance metrics and athlete feedback in college sports
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 Te...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-13949-6 |
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| Summary: | 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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| ISSN: | 2045-2322 |