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The Power-Flex by Soul Mobility is intended to provide enhanced mobility to disabled persons who are capable of operating a powered and manual wheelchair, by providing powered mobility to manual wheelchairs.

Power-Flex is a power-assist drive that can be attached to a manual wheelchair frame, which enables a manual wheelchair to perform like an electrical wheelchair. The Power-Flex drive system includes two drive wheels, two caster wheels for stability, batteries, a control unit, a power supply and a manual tilt mechanism which also acts as an anti-tip preventative. The Power-Flex connects via the manual chair frame standard axle receivers and the rear manual tilt clamp which attaches to the rear rigidizer bar of the manual wheelchair. The manual wheelchair and frame are not part of the drive system and is provided by the customer. The control unit is the steering device of the system

This document is a 510(k) Premarket Notification for a powered wheelchair add-on device, the "Power-Flex." The purpose of a 510(k) submission is to demonstrate that a new device is substantially equivalent to a device already legally marketed (a predicate device), and thus does not require a full Premarket Approval (PMA).

The provided text does not contain acceptance criteria or a study proving that the device meets acceptance criteria in the way typically found for medical imaging AI devices (e.g., performance metrics like sensitivity, specificity, AUC). This document pertains to a physical medical device, a powered wheelchair add-on, and its substantial equivalence to a predicate device based on engineering and safety standards, rather than diagnostic accuracy or algorithmic performance.

Therefore, the requested information cannot be fully extracted or accurately inferred from the provided text for the following reasons:

• No "Acceptance Criteria" table in the AI/diagnostic sense: The document focuses on compliance with recognized consensus standards (ANSI RESNA WC-1 and WC-2, ISO 7176) and demonstrating similarities to a predicate device (AMP). The "acceptance criteria" for this type of device are adherence to these engineering and safety standards, rather than diagnostic performance metrics.
• No "Study" in the clinical trial sense for AI validation: The "study" here is a comparison of product specifications and compliance with established performance standards for wheelchairs. There isn't a "test set" of patient data or "ground truth" adjudicated by experts in the context of an AI/diagnostic study.
• No "human-in-the-loop" or "standalone algorithm" performance: These concepts are irrelevant to a powered wheelchair add-on.

However, I can extract information related to the device's adherence to standards and its comparison to a predicate device, which serves a similar function to "proving it meets acceptance criteria" for this type of device.

Re-interpreting for a physical medical device (Powered Wheelchair Add-on):

In the context of this 510(k) submission for a physical medical device, "acceptance criteria" and "study" refer to the demonstration of compliance with recognized performance standards and safety requirements, and to the comparison of the new device's features and performance against a legally marketed predicate device.

Here's an analysis based on that reinterpretation:

Acceptance Criteria and Device Performance (Reinterpreted for a Physical Device)

The "acceptance criteria" for this device are its compliance with various ANSI RESNA and ISO 7176 standards, and its substantial equivalence to the predicate device in terms of safety and effectiveness. The "reported device performance" is how the Power-Flex compares to the predicate and demonstrates compliance with these standards.

1. Table of "Acceptance Criteria" and "Reported Device Performance"

Since there isn't an explicit "acceptance criteria" table with performance metrics in the AI sense, I will create a table summarizing key comparisons between the proposed device (Power-Flex) and the predicate device (AMP), along with the "Comments" from the submission which serve as the "reported performance" against implicit regulatory expectations.

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

• This is not applicable in the context of an AI/diagnostic study. For a physical device like this, "testing" would refer to engineering and functional tests against the specified ANSI RESNA/ISO standards. The text implies that such testing was conducted to demonstrate compliance (e.g., "Power-Flex tested to 275lb weight per standards," "Power-Flex was tested and passed applicable standards").
• There is no "test set" of patient data.
• Data provenance: Not applicable in this context. The testing would be carried out by the manufacturer or accredited labs to the specified engineering standards.

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. "Ground truth" for this device is defined by passing the engineering and safety performance requirements specified by the ANSI RESNA and ISO 7176 standards, not by expert interpretation of subject data. Compliance is assessed through physical testing and technical specification comparison, rather than human expert consensus on a diagnostic outcome.

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

• Not applicable. There is no human adjudication process of a "test set" as would be found in an AI diagnostic study. Device compliance is determined by objective measurements against established engineering standards.

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 diagnostic device.

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

• Not applicable. This is not an AI diagnostic device. Its function is direct physical assistance.

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

• The "ground truth" for this device's safety and effectiveness is defined by its adherence to the performance and safety requirements outlined in international and national consensus standards (ANSI RESNA WC-1:2019, WC-2:2019, and ISO 7176 Standards). This includes tests for stability, braking, energy consumption, speed, strength, climatic conditions, obstacle climbing, and electromagnetic compatibility. Engineering test results demonstrating compliance with these standards serve as the "ground truth."

8. The sample size for the training set:

• Not applicable. This is a physical device, not an AI model requiring a "training set." The design and manufacturing process would involve engineering iterations and testing against specifications, rather than data-driven training.

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

• Not applicable. See point 8. The "ground truth" for developing such a device revolves around engineering principles, material science, and adherence to established regulatory and consensus standards for medical devices and wheelchairs, rather than a labeled dataset.

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