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The Rapid Reboot Compression Therapy System is indicated for the temporary relief of minor muscle aches and pains and for temporary increase in circulation to the treated areas in people who are in good health. The Rapid Reboot Compression Therapy System simulates kneading and stroking of tissues by using an inflatable garment.

Rapid Reboot Compression Therapy System ("Rapid Reboot") consists of an air pump, air pressure sensor, and sleeves working together as one unit. The air pump is connected to the dedicated sleeves via a series of hoses; each sleeve has four (4) compression chambers. The compression massage direction is from limb end to body center. By inflating the air chambers sequentially and then deflating as one cycle, the pressure can be adjusted to avoid any discomfort to the patient. The sleeve works under the action of sensor and microprocessor. Software controls the timing and pressure reflected by the sensor, cycling airflow into and out of the sleeves to compress body

The provided text describes the "Rapid Reboot Compression Therapy System" and its substantial equivalence determination. However, it does not include detailed acceptance criteria or a specific study proving the device meets those criteria in the way typically found for AI/ML-driven medical devices (e.g., performance metrics like sensitivity, specificity, or AUC, alongside sample sizes for test and training sets, expert qualifications for ground truth, etc.). This document solely focuses on demonstrating substantial equivalence to predicate devices based on indications for use, technological characteristics, and compliance with general medical device standards.

Therefore, most of the requested information regarding acceptance criteria and performance study details for an AI/ML device is not present in the provided text.

Here's an attempt to answer based only on the available information:

1. A table of acceptance criteria and the reported device performance

The document does not specify quantitative acceptance criteria in terms of performance metrics (e.g., sensitivity, specificity, accuracy) or device performance against such metrics. Instead, it demonstrates compliance with general medical device standards and substantial equivalence to predicate devices.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not applicable. The document describes performance testing based on compliance with general safety, EMC, and biocompatibility standards for the device itself, not on analyzing patient data or clinical images. It's a physical device, not an AI/ML diagnostic or prognostic system.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. This is not a study requiring expert-established ground truth on patient data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No, an MRMC study was not done. The device is a compression therapy system, not an AI-assisted diagnostic tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. The device is a physical therapy system, not an algorithm.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

Not applicable. Ground truth as typically understood for AI/ML performance on clinical data is not relevant here. The "ground truth" equivalent would be the established requirements of the referenced IEC and ISO standards that the device was tested against.

8. The sample size for the training set

Not applicable. The device is not an AI/ML model trained on data.

9. How the ground truth for the training set was established

Not applicable.

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