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>
Client Challenge



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</reference>

<statements>
1. The clearest engineering demonstration is a cloud–edge–end system that fuses 30 Hz video, 100 Hz inertial data and 1 Hz heart-rate data to deliver edge feedback in about 176–180 ms with 89.4–93.2% motion-recognition accuracy in a study of 45 college students
2. The practical conclusion is that construction is feasible and partially operational, while application claims require stronger independent validation of device validity, privacy, usability, teacher integration and longitudinal learning outcomes
3. In physical education, these functions are translated into movement correction, exercise-load adjustment, personalized training prescriptions and safety monitoring
4. The cited architectures aim to convert multimodal data into instructional decisions: edge feedback for immediate movement correction, cloud analysis for long-term personalization, dashboards for teacher intervention and recommendation engines for individualized exercise plans
5. The most complete construction evidence is a three-tier cloud–edge–end system. Terminal devices collect multimodal data, edge nodes perform lightweight real-time analysis and instant feedback, and the cloud handles large-scale processing, deep model training and long-term personalized analysis
6. The terminal layer in that prototype uses Logitech C920 Pro cameras at 30 Hz, Bosch BNO055 IMUs at 100 Hz and Polar H10 chest straps at 1 Hz
7. The same study reports that its sub-200 ms average response is more than 65% lower than a pure-cloud solution
8. 30 Hz video, 100 Hz IMU and 1 Hz heart-rate data in the cloud–edge–end prototype
9. lightweight motion recognition and instant feedback, with average edge response of 176–180 ms
10. large-scale data processing, model training and long-term personalized analysis
11. The cloud–edge–end prototype combines vision, inertial and physiological streams
12. The cloud–edge–end system is an experimental prototype involving 45 college students
13. In basketball, it extracts key joint points from video and uses a lightweight motion recognition model to output motion scores and correction suggestions
14. In gymnastics, it compares IMU and camera key-point sequences against a standard template using Euclidean-distance similarity
15. In long-distance running, it emphasizes step frequency, heart rate and exercise intensity to verify training safety and real-time adjustment
16. The system reports average edge response time of 176–180 ms, motion-recognition accuracy of 89.4–93.2%, stability index of 0.88–0.91 and user satisfaction of 4.3–4.6 points before cloud personalization is enabled
17. After enabling cloud-based personalized analysis, accuracy increased by about 3.4 percentage points, response time decreased by 10.8 ms and satisfaction increased by 0.6 points
18. Across five iterative rounds, response time fell from 192.4 ms to 176.2 ms and accuracy rose from 90.5% to 93.2%
19. Under high-latency network plus high-complexity tasks, response time was 197.5 ms, accuracy was 89.6% and stability was 0.85
20. The same source states that recognition accuracy for complex movements in high-difficulty programs such as gymnastics is slightly lower than in basketball and long-distance running
21. It also notes that in high-speed flips, IMUs are susceptible to instantaneous high-impact interference and spatiotemporal alignment with visual data becomes difficult
22. The paper further states that current personalized training prescriptions are mainly based on exercise scores and physiological indicators and have not fully incorporated psychological states and environmental conditions
23. In classroom prototypes, fusion supports low-latency feedback and sport-specific assessment
24. But the evidence does not show that benchmark coaching models have been integrated into validated school tutoring loops, because the coaching papers report dataset-level results, while the school systems report operational deployment metrics
25. In PE, the analogous “worked example” appears as expert demonstration or movement template: the cloud–edge–end system compares student movement to a standard template in gymnastics and uses motion scores and correction instructions in basketball
26. The cloud–edge–end system uses heart-rate data to adjust exercise intensity and avoid sports risk
27. The cloud–edge–end study involved 45 college students in basketball, gymnastics and long-distance running classes
28. A prototype can report 89.4–93.2% motion-recognition accuracy and 176–180 ms edge response
29. The cloud–edge–end prototype acknowledges high-impact interference in flips and lower accuracy in gymnastics
30. The cloud–edge–end paper names privacy-security verification as an unresolved systematic challenge in large-scale teaching
31. The cloud–edge–end paper states that existing research has limitations in systematic integration, including lack of end-to-end design from multimodal perception to intelligent decision-making, insufficient cloud–edge–end collaboration and inadequate verification of scalability and privacy-security in large-scale teaching
32. The cloud–edge–end paper states that existing research has limitations in systematic integration, including lack of end-to-end design from multimodal perception to intelligent decision-making, insufficient cloud–edge–end collaboration and inadequate verification of scalability and privacy-security in large-scale teaching. Its own personalization module still does not fully incorporate psychological states and environmental conditions
33. Whether coaching-generation models can move from benchmark datasets to classroom loops with acceptable latency, privacy protection and teacher oversight remains open, given the separation between benchmark metrics and deployment metrics
34. The architecture is well specified: terminal sensing, edge inference, cloud personalization, learner modeling, adaptive recommendation, safety monitoring and teacher dashboards
35. Technical performance evidence exists for low-latency motion recognition and personalization gains in a college-student prototype
36. A defensible next step is to combine the cloud–edge–end architecture
37. A defensible next step is to combine the cloud–edge–end architecture, the digital-base and platform layers, deployed gateway stacks and coaching-generation models under a validation regime that includes device concurrent validity, privacy and security governance
</statements>

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