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    },
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        "result": "unsupported"
    }
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<reference>
Multimodal Deep Learning and Reinforcement Learning Framework for Personalized Sports Training and Recovery Optimization
							| Informatica



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Vol. 49 No. 32 (2025): Online-only issue

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Online-only

Multimodal Deep Learning and Reinforcement Learning Framework for Personalized Sports Training and Recovery Optimization

Abstract

In this paper, an intelligent exercise training and recovery system based on multi-modal data fusion is proposed, which aims to optimize the training plan through an AI-driven model and predict the training load and recovery requirements in real time. The system integrates athletes' physiological signals, motion images and environmental data, totaling a data set of 800 athletes. The Xception model is used to extract the spatial characteristics of the moving image, and the BiLSTM model is used to analyze the dynamic characteristics in the time series data to achieve accurate prediction of training load and recovery time. On this basis, the deep deterministic policy gradient (DDPG) reinforcement learning algorithm is used to dynamically adjust the training intensity, duration and frequency based on real-time feedback. The experimental results show that when tested under different environmental conditions, the mean square error (MSE) of the training load prediction of the system is less than 0.05, the determination coefficient (R2) is close to 0.99, and the recovery time prediction is stable between 0.018 and 0.022 in the 10-fold cross-verification. The R2 value is as high as 0.98. Compared with the traditional training system, the injury rate of athletes in this system is significantly reduced, with an average injury rate of only 0.07, much lower than the traditional system of 0.12 to 0.22. Studies have shown that the system has a wide range of application potential, especially in high-intensity training and personalised training optimisation in complex environments.

References

Yang Guoqing. Integration of periodization: a new way of thinking on the reform of contemporary sports training model[J]. Sports Science, 2020, 40(4): 3-14.

Hu Haixu, Jin Chengping. Research progress of sports training periodization theory and its practical implications[J]. Journal of Beijing Sport University, 2020, 43(1): 114-124.

Wang Jianing, Ao Yingfang. Winter Olympics and sports injuries in major winter sports[J]. Science & Technology Review, 2020, 38(6): 11-24.

Liu Hongwei, Li Jianjun, Yang Mingliang, et al. Analysis of sports injury data in the Winter Olympics/Winter Youth Olympic Games[J]. Chinese Journal of Rehabilitation Theory and Practice, 2020, 26(10): 1209-1216.

Li Haipeng, Chen Xiaoping, He Wei, et al. Technology promotes competitive sports: Application and development of wearable devices in sports training[J]. Journal of Chengdu Sports University, 2020, 46(3): 19-25.

Ding Chenchen, Liu Weiguo. Quantitative evaluation of movement disorder characteristics in early Parkinson's disease using multi-point wearable devices[J]. Journal of Nanjing Medical University (Natural Science Edition), 2024 (12): 1708-1714.

Sharp T, Grandou C, Coutts AJ, et al. The effects of high-intensity multimodal training in apparently healthy populations: A systematic review[J]. Sports medicine-open, 2022, 8(1): 43.

Zheng N, Sun M, Yang Y. Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network[J]. Computational Intelligence and Neuroscience, 2022, 2022(1): 5398932.

Naureen Z, Perrone M, Paolacci S, et al. Genetic test for the personalization of sport training[J]. Acta Bio Medica: Atenei Parmensis, 2020, 91(Suppl 13).

Wackerhage H, Schoenfeld B J. Personalized, evidence-informed training plans and exercise prescriptions for performance, fitness and health[J]. Sports Medicine, 2021, 51(9): 1805-1813.

Gennarelli SM, Brown SM, Mulcahey M K. Psychosocial interventions help facilitate recovery following musculoskeletal sports injuries: a systematic review[J]. The Physician and Sportsmedicine, 2020, 48(4): 370-377.

Song H, Montenegro-Marin CE, Krishnamoorthy S. Retracted article: Secure prediction and assessment of sports injuries using deep learning based convolutional neural network[J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(3): 3399-3410.

Zhang Jiahui, Wang Jianping, Chen Yuni. Consumer dissatisfaction survey on sports protection smart wearable devices[J]. Journal of Zhejiang Textile and Fashion Vocational Technical College, 2020, 19(2): 15-20.

Jialing Xie, Yushun Gong, Liang Wei, et al. A motion-resistant heart rate detection algorithm for wearable devices [J]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi= Journal of Biomedical Engineering, 2021, 38(4): 764.

Hu Haixu, Jin Chengping. Research progress of sports training periodization theory and its practical implications[J]. Journal of Beijing Sport University, 2020, 43(1): 114-124.

Wei Xiaobin, Chen Hui, Wang Nianci, et al. A review of research on football training load based on quantitative data[J]. Journal of Chengdu Sports University, 2024, 50(6): 106-116.

Zhang Q, Zhang X, Hu H, et al. Sports match prediction model for training and exercise using attention-based LSTM network[J]. Digital Communications and Networks, 2022, 8(4): 508-515.

Zhang T, Fu C. Application of improved VMD‐LSTM model in sports artificial intelligence[J]. Computational Intelligence and Neuroscience, 2022, 2022(1): 3410153.

Fuchs F, Song Y, Kaufmann E, et al. Super-human performance in gran turismo sport using deep reinforcement learning[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 4257-4264.

Li Zicong, Zeng Fanzhong, Wu Zihui, et al. Reinforcement learning training method for virtual table tennis players[J]. Journal of Computer-Aided Design & Graphics, 2020, 32(6): 997-1008.

Qadeer A, Lee M J. Deep-deterministic policy gradient based multi-resource allocation in edge-cloud system: a distributed approach[J]. IEEE Access, 2023, 11: 20381-20398.

Labbaf Khaniki MA, Samii A, Tavakoli-Kakhki M. Adaptive PID controller using deep deterministic policy gradient for a 6D hyperchaotic system[J]. Transactions of the Institute of Measurement and Control, 2025, 47(3): 572-584.

Authors

Zhang Shunchao

School of Physical Education, Pingdingshan University, Pingdingshan City, 467000, Henan, China

Niu Qing

Faculty of Mathematics and Statistics,Pingdingshan University, Henan 467000, China

DOI:

https://doi.org/10.31449/inf.v49i32.8605

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Published

08/26/2025

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Vol. 49 No. 32 (2025): Online-only issue

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How to Cite

Shunchao, Z., & Qing, N. (2025). Multimodal Deep Learning and Reinforcement Learning Framework for Personalized Sports Training and Recovery Optimization.
Informatica
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49
(32).
https://doi.org/10.31449/inf.v49i32.8605

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</reference>

<statements>
1. Sports intelligent tutoring and learning guidance systems increasingly rely on multimodal data fusion to deliver real-time feedback, personalized interventions, and safer training experiences. Recent work in physical education, sports training, and intelligent tutoring demonstrates that combining heterogeneous data streams (video, audio, wearable sensors, textual instructions, and knowledge graphs) with deep learning and reinforcement learning yields higher recognition accuracy, more precise guidance, and measurable improvements in learning and health outcomes compared with single-modality or rule-based systems.
2. Recent ITS research introduces automatic construction of multimodal knowledge graphs from course videos and textbooks and Retrieval-Augmented Generation (RAG) to ground large language models, improving factuality and explainability of tutoring dialogue.
3. Structured knowledge from textbooks and lecture videos into knowledge graphs for query answering and tutoring.
4. Deng and Yuan (Frontiers in Computer Science 2026) propose an ITS that automatically constructs multimodal knowledge graphs from course videos and textbook PDFs and uses hybrid graph + vector Retrieval-Augmented Generation for tutoring. FFmpeg and Whisper extract and transcribe audio from instructional videos; PDF parsing, OCR, and layout analysis extract structured text, diagrams, and formulas from textbooks. BERT + CRF models perform entity recognition, and BERT-based classifiers perform relation extraction, followed by cross-modal entity disambiguation and hierarchical clustering-based entity fusion.
5. Knowledge is stored both in a Neo4j graph database and a vector database (HNSW index), supporting hybrid retrieval that combines Cypher graph queries with dense vector similarity. Retrieved subgraphs and text chunks ground an LLM via RAG to produce accurate, explainable answers, with a fusion score balancing graph provenance and semantic similarity. Case studies in a “Stacks and Queues” data structures course yield a knowledge graph with 152 entities and 243 relations; entity extraction achieves F1 88.1%, relation extraction F1 83.6%, and Q&A evaluation shows higher accuracy, relevance, and interpretability than text-only RAG and manual KG baselines. While non-sports, this architecture offers a pattern for the symbolic tutoring component of sports guidance systems.
6. Knowledge and tutoring layer: knowledge graphs, recommendation engines, RAG-based LLMs, and rule-based logic perform tutoring, explanation, and resource recommendations.
7. This layered design supports modularity, scalability, and edge-cloud collaboration: latency-sensitive inference (e.g., motion feedback) can run at the edge, while heavier knowledge graph processing and long-term model training run in the cloud.
8. Graph + vector retrieval fusion: Deng’s hybrid Graph RAG uses weighted fusion of Neo4j graph retrieval and HNSW vector retrieval to ground LLM answers with both logical and semantic evidence.
9. Empirical ablations consistently show that attention-based and hybrid fusion strategies outperform simple concatenation and late fusion, particularly in complex tasks like anomaly detection, behavior analysis, and multimodal tutoring.
10. Course videos, training manuals, and sports textbooks provide symbolic domain knowledge.
11. Automatically construct a sports knowledge graph from multimodal educational resources (videos of drills, coaching lectures, and textbooks) using entity and relation extraction; include entities for skills, drills, anatomical structures, training principles, and common mistakes, following Deng and Li’s sports knowledge graph work.
12. Store knowledge in Neo4j and encode supporting text in a vector database; use hybrid graph + vector retrieval to ground a tutoring LLM via RAG for both conceptual questions (e.g., “Why is stride frequency important?”) and procedural advice (“How to adjust running form?”).
13. Higher recommendation and tutoring quality: Multimodal analysis and multi-objective optimization (knowledge graphs + behavioral data) can yield recommendation F1 scores around 0.85 and user satisfaction scores above 4.5/5 in teacher development contexts.
14. Studies of sports teachers and coaches emphasize that AI systems should complement, not replace, human expertise, providing objective measurements, pattern detection, and personalized recommendations while leaving final decisions and nuanced motivational strategies to humans. Interfaces should be designed to support rapid inspection, override, and reconfiguration, and to incorporate user feedback into continuous learning loops, similar to Deng’s human-in-the-loop knowledge graph review.
15. Future work will need to expand datasets, refine fusion strategies, and deepen human–AI collaboration, but the empirical evidence already supports practical deployment of multimodal sports intelligent tutoring and guidance systems in real-world settings.
</statements>

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