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The iBOT® Personal Mobility Device ("iBOT® PMD") is intended to provide indoor mobility to persons restricted to a sitting position who meet the requirements of the user assessment and training certification program. The device allows for the option to climb stairs. Companions who are required to provide assistance during Assisted Stair Climbing Mode must meet the requirements of the training certification program. The iBOT® Personal Mobility Device ("iBOT® PMD") Occupied Transport option is indicated for providing persons, unable to transfer from their wheelchair into a standard factory motor vehicle seat, the option for transportation while seated in their iBOT® PMD wheelchair.

The proposed device is an update to the previously cleared device Next Generation iBOT® (K172601). The device retains all the following from the original device including:

The device is a multi-mode powered wheelchair that enables users to manewer in confined spaces, climb curbs, stairs, and other obstacles. The device is intended to provide indoor mobility, including stair climbing, to persons limited to a seated position who are capable of operating a powered wheelchair.

The device still includes active stabilization in modes and allows for traversing aggressive and difficult terrain and operation at an elevated seat height offers benefits in activities of daily living (e.g. accessing higher shelves) and interaction with other people at "eye level" while either stationary or moving.

The proposed device still utilizes the primary components of a stair climbing power wheels, frame, sealed electronics, sensors, battery packs, motors, seative stability system and battery charger. It can also be produced without the stair climbing function.

In addition, the device will incorporate the following updates to the Next Generation iBOT® design:

• Allows for occupied transportation in a motor vehicle ●
• Incorporates the use of an alternate seating system, mounting and peripherals ●
• . Wheel gear train changed from spur to helical
• Redesigned brake release lever mechanism
• Changes of materials / processes in support of Design for Manufacturing efforts .
• Software revisions / changes ●
• Incorporates change of the Trade name to iBOT® PMD ●
• Updates contraindications to increase clarity ●

The provided text describes a 510(k) summary for the iBOT® Personal Mobility Device ("iBOT® PMD"). This document focuses on demonstrating substantial equivalence to a predicate device (Next Generation iBOT®, K172601) and a reference device (Permobil F5 Corpus VS, K191874) rather than presenting a standalone study with acceptance criteria and a detailed breakdown of device performance against those criteria in a typical clinical study format.

However, based on the information provided, I can infer and summarize some "acceptance criteria" through the standards the device was tested against and the performance characteristics listed in the comparison tables. The study that "proves" the device meets these criteria is the performance testing and bench testing conducted.

Here's a breakdown of the requested information, understanding that this is a regulatory submission for substantial equivalence and not a standalone clinical trial report:

1. Table of acceptance criteria and the reported device performance

The acceptance criteria are implicitly defined by the recognized standards the device complied with and the performance characteristics being similar to or better than the predicate device. The "reported device performance" is essentially the device's compliance with these standards and its characteristics as described.

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

The document lists performance testing conducted for regulatory compliance (bench testing, software testing, usability testing). It does not provide specific sample sizes for these tests in terms of human subjects or a large dataset in the way a traditional clinical study would for efficacy.

• Bench Testing: These tests are typically conducted on a representative sample of devices or components, but specific numbers are not provided. The provenance is implied to be from the manufacturer's testing facilities as part of a regulatory submission.
• Software Testing: This involves testing the software code for functionality, reliability, and security. No sample size of users or data is mentioned.
• Usability Testing: The text states a "usability evaluation was conducted on the elements of the device (selection of docking mode and use of pin and loop interfaces for occupied transportation) which have changed from the predicate device." It does not specify the number of participants nor their characteristics.
• Data Provenance: Not explicitly stated as country of origin, but implied to be from the manufacturer's internal testing as part of the 510(k) submission process. Such tests are generally prospective for the specific purpose of the submission.

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

This information is not provided in the document. For device regulatory submissions like this, "ground truth" often refers to adherence to established engineering standards rather than expert consensus on medical images or diagnoses. For usability, the "ground truth" would be the successful use of the device without significant errors or hazards, which is evaluated by the testing team and potentially observed users, but specific expert qualifications are not listed.

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

This information is not provided in the document. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies for diagnostic accuracy to resolve discrepancies among multiple expert readers. This document describes performance and safety testing of a physical device, which typically relies on objective measurements against standards or pass/fail criteria, not expert adjudication in the medical imaging sense.

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 comparative effectiveness study was not done. This device is a personal mobility device (stair-climbing wheelchair), not an AI-powered diagnostic tool. Therefore, the concept of "human readers improving with AI vs without AI assistance" is not applicable here.

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

This question is not applicable as the iBOT® PMD is a physical, human-operated mobility device, not an algorithm, and its performance inherently involves human interaction. Even for software components, "standalone" in this context refers to the software's function within the device, not an isolated algorithm without human control.

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

For the iBOT® PMD, the "ground truth" is primarily based on:

• Compliance with recognized industry standards: (e.g., ISO, IEC, UL, UN, RESNA WC-1). These standards define objective test methods and acceptable performance limits for various device characteristics (stability, speed, obstacle climbing, safety, etc.).
• Engineering specifications and safety analysis: The design is verified against internal specifications and risk assessments (FMEA - Failure Mode and Effects Analysis mentioned for occupied transport) to ensure safety and effectiveness.
• Functional performance: Testing to confirm the device performs its intended functions (e.g., driving range, maximum speed, obstacle climbing) as specified.
• Usability: Evaluation focuses on whether users can safely and effectively operate the device, particularly the new features.

There is no mention of ground truth established by expert consensus (e.g., radiologists for diagnosis), pathology, or long-term outcomes data in the context of this 510(k) summary.

8. The sample size for the training set

This information is not applicable. The iBOT® PMD is a physical, mechanical, and software-controlled device. It does not appear to utilize machine learning or AI that would require a "training set" of data in the common sense (e.g., for image recognition or predictive analytics). The "training" referred to in the document relates to the human user's certification program for operating the device, not an algorithm's training data.

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

This question is not applicable as there is no "training set" for an algorithm. The "training certification program" for users implies that the "ground truth" for successful user operation would be adherence to the prescribed procedures and demonstrating competency as defined by Mobius Mobility, but the details of this program's evaluation are not discussed.

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