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>
ERROR: The request could not be satisfied



503 ERROR

The request could not be satisfied.

The Lambda function associated with the CloudFront distribution is invalid or doesn't have the required permissions.
We can't connect to the server for this app or website at this time. There might be too much traffic or a configuration error. Try again later, or contact the app or website owner.

If you provide content to customers through CloudFront, you can find steps to troubleshoot and help prevent this error by reviewing the CloudFront documentation.

Generated by cloudfront (CloudFront)
Request ID: h4UndaOeLc9kNHWgUI4iRqgdxhAmbNIdqduZr6dIMTxf_20FZs1zJQ==
</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. Systems such as CAM-Vtrans use multimodal visual–text fusion to provide real-time action feedback, while others use multimodal deep learning to analyze sports teachers’ behavior and recommend continuing education resources. Together, these works motivate an integrated sports intelligent tutoring system that can reason over both symbolic knowledge and sensor-derived signals.
3. Multimodal data fusion integrates information from multiple sensor or content modalities—visual, audio, textual, kinematic, and physiological—into a unified representation for downstream analysis and decision-making.
4. Wearable signals such as steps, heart rate, acceleration, and galvanic skin response for workload, fatigue, and anomaly detection.
5. Empirical studies in physical education and sports show that multimodal fusion consistently outperforms single-modality approaches for behavior classification, anomaly detection, and recommendation accuracy.
6. Dong (IJDST 2023) proposes an intelligent physical education teaching evaluation system that uses wearable devices (e.g., Xiaomi bracelets) to collect time-series step counts and heart rate data in school PE classes. A Transformer-based deep network analyzes step sequences to classify motion performance levels, while a hierarchical fusion network based on Transformer encoders integrates steps and heart rate for anomaly detection during teaching activities.
7. Transformer-based step analysis achieves the highest accuracy (93.54%) compared with RNN, GRU, and LSTM (75.87–92.02%). For anomaly detection, hierarchical multimodal fusion (steps + heart rate) yields recognition accuracy of 98.15%, outperforming single-modality and serial fusion baselines (82.85–97.24%). These results show that multimodal fusion significantly improves detection of excessive fatigue and unsafe exercise states, enabling fine-grained guidance to avoid injuries.
8. Sensing layer: wearable devices, cameras, microphones, and environmental sensors capture raw multimodal data.
9. Preprocessing layer: synchronization, resampling, denoising, segmentation, and feature extraction transform raw signals into structured representations.
10. Core analysis layer: deep learning models (CNN, LSTM, Transformer, GCN, cross-attention, hierarchical attention) perform action recognition, anomaly detection, behavior classification, and state estimation.
11. Sequence-level fusion with Transformers: Dong uses dual Transformer encoders for steps and heart rate, with hierarchical cross-fusion to improve anomaly detection accuracy to 98.15%.
12. 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.
13. Student-focused physical education guidance: monitoring steps, heart rate, and movement quality during PE classes; detecting abnormal states; providing immediate feedback and adjusting activity intensity.
14. Wearable devices (bracelets, IMUs, chest straps) collect steps, heart rate/HRV, acceleration, galvanic skin response, and possibly EMG for select sports.
15. Use Transformer-based encoders for time-series wearable data (steps, heart rate) with hierarchical cross-fusion to detect anomalies and classify workload states, building on Dong’s design.
16. Incorporate rule-based safety constraints (e.g., heart rate >90% of age-predicted max triggers mandatory rest) to ensure robust safety during policy exploration.
17. Improved recognition and diagnosis: Action recognition accuracies above 95% and anomaly detection accuracies around 98% are achievable with Transformer-based and hybrid fusion models, enabling fine-grained detection of technique errors and unsafe workload patterns.
18. Systems must be robust to sensor failures, noise, and domain shifts across sports and populations. Techniques such as adaptive preprocessing, domain adaptation, and robust fusion are critical. Cross-sport generalization may require sport-specific models with shared backbones and sport-specific heads.
19. 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>

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