You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
Multimodal Data Fusion and Adaptive Optimization in Tennis Training Based on Deep Deterministic Policy Gradient and IoT Sensors
							| Informatica



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Vol. 49 No. 25 (2025): Online-only issue

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Online-only

Multimodal Data Fusion and Adaptive Optimization in Tennis Training Based on Deep Deterministic Policy Gradient and IoT Sensors

Abstract

This paper proposes a novel framework integrating IoT technologies, multimodal sensor networks, and the Deep Deterministic Policy Gradient (DDPG) algorithm for intelligent tennis training. We employ the DDPG algorithm for adaptive training adjustments, which dynamically optimizes the training policy based on real-time feedback. Experimental evaluation on 10 athletes shows that the DDPG algorithm improves performance metrics in multiple training scenarios, increasing the average game score from 50 to 80 points and reducing the error rate in high-pressure scenarios from 13% to 6%. The system’s success rate reached 85%, with swing stability enhanced by 27% (0.1 rad deviation). These quantifiable outcomes highlight the framework’s effectiveness in optimizing training strategies, with potential applications in industrial automation and healthcare monitoring.

Author Biography

Yang Zhi-jun, Shangqiu Normal University

School of Physical Education, Shangqiu Normal University, Shangqiu, 476000, Henan, China

References

References:

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Robilova SM, Patidinov K D. Physical training of handball and its comparative analysis practitioners[J]. Asian Journal of Research in Social Sciences and Humanities, 2022, 12(4): 173-177.

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

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.

Zhi Jixin , Zhao Zijian, Cui Shuqin, et al. The driving mechanism of sports participation behavior from the perspective of social connection based on data analysis of tennis participants[J]. Journal of Shanghai Sport University, 2023, 47(6): 76-87.

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Ramkumar PN, Luu BC, Haeberle HS, et al. Sports medicine and artificial intelligence: a primer[J]. The American Journal of Sports Medicine, 2022, 50(4): 1166-1174.

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Vellela SS, Rao MV, Mantena SV, et al. Evaluation of Tennis Teaching Effect Using Optimized DL Model with Cloud Computing System[J]. International Journal of Modern Education and Computer Science (IJMECS), 2024, 16(2): 16-28.

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Wang X, Wang S, Liang X, et al. Deep reinforcement learning: A survey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 35(4): 5064-5078.

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Yu Y, Tang J, Huang J, et al. Multi-objective optimization for UAV-assisted wireless powered IoT networks based on extended DDPG algorithm[J]. IEEE Transactions on Communications, 2021, 69(9): 6361-6374.

Chang CC, Tsai J, Lin JH, et al. Autonomous driving control using the ddpg and rdpg algorithms[J]. Applied Sciences, 2021, 11(22): 10659.

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Authors

Yang Zhi-jun

Shangqiu Normal University

DOI:

https://doi.org/10.31449/inf.v49i25.8485

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Published

07/03/2025

Issue

Vol. 49 No. 25 (2025): Online-only issue

Section

Online-only

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. Under this license, others may share and adapt the work for any purpose, provided appropriate credit is given and changes (if any) are indicated.

Authors may deposit and share the submitted version, accepted manuscript, and published version, provided the original publication in Informatica is properly cited.

How to Cite

Zhi-jun, Y. (2025). Multimodal Data Fusion and Adaptive Optimization in Tennis Training Based on Deep Deterministic Policy Gradient and IoT Sensors.
Informatica
,
49
(25).
https://doi.org/10.31449/inf.v49i25.8485

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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.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.