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The device is intended for medical purposes to provide mobility to persons restricted to a seated position.

The Be-Mobile 3-wheeled Electric Scooter DK S200 is an indoor / outdoor electric scooter that is battery operated. It has a base with 3-wheeled with a seat. The movement of the scooter is controlled by the rider who uses hand controls located at the top of the steering column. The device can be disassembled for transport and is provided with an onboard battery charger.

The provided text is a 510(k) summary for the "Be-Mobile 3-wheeled Electric Scooter, DK S200". It focuses on establishing substantial equivalence to a predicate device rather than detailing specific acceptance criteria and performance studies in the way one might for a novel AI/software medical device.

Therefore, many of the requested sections related to AI/software device performance, ground truth, expert adjudication, and MRMC studies are not applicable and cannot be answered from the provided document.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not specify quantitative acceptance criteria in terms of performance metrics (e.g., accuracy, sensitivity, specificity) for the device's intended medical purpose (providing mobility). Instead, it focuses on demonstrating safety and functionality equivalence to a predicate device through compliance with specific standards.

2. Sample size used for the test set and the data provenance

Not applicable. This device is a physical medical device (electric scooter), not a software/AI device that would typically have a "test set" of data. Performance testing appears to be based on compliance with established engineering and safety standards, likely involving a single device or a small representative sample.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. Ground truth as typically understood for AI/software pertains to expert labeling of data. For this physical device, "ground truth" would be compliance with engineering standards as verified by testing laboratories (e.g., SGS, UL). The document does not specify the number or qualifications of individuals involved in these testing processes, beyond naming the certifying bodies.

4. Adjudication method for the test set

Not applicable. There is no "test set" in the context of expert adjudication for this type of device submission.

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

Not applicable. This is not an AI-powered device, and therefore no MRMC study looking at human reader performance improvement is relevant.

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

Not applicable. This is not an algorithm or AI device.

7. The type of ground truth used

For safety and functional equivalence:

• Compliance with recognized electrical and mechanical standards: EMC Report ANSI / RESNA WC/Vol.2-1998, CISPR 11: 1990, EN61000-3-2: 1995, IEC61000-3-3: 1995 for general performance and electrical safety.
• Safety certifications: UL certification for electronic systems (controller, batteries, recharge).
• Material testing: SGS certification for the resistance ignition test of the back upholstery.

8. The sample size for the training set

Not applicable. This is not an AI/machine learning device that involves a "training set."

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

Not applicable. This is not an AI/machine learning device.

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