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The device is a motor-driven, and indoor transportation vehicle with the intended use to provide mobility to a disabled or an elderly person limited to a seated position.

The TUNG KENG Electrically Powered Wheelchair, Joy Rider, is suitable for indoor uses. It is characterized by high portability in comfortable travel, small storage and convenient delivery in daily life. As folding of Joy Rider only takes just a few seconds, so this model increases opportunities for travel and provides small storage. To use Joy Rider makes your travel in any place and at any moment! It is an individual transportation vehicle for disabled and elderly people that experience difficulties in walking. The Joy Rider comes as preassembled, designed to be lightweight, easily maneuvered, highly portable, and most importantly, safe and comfortable.

It is driven by two brushless DC motors, and contains two foldable armrests, a seat belt, a backrest, a seat cushion and small with light texture, a foldable frame, two rear driving wheels with hub motor/electromagnetic brake assemblies, two pivoting casters, one Lithium-ion battery, an off-board battery charger, a control panel, and an electrical controller.

The device is powered by one 24 VDC/ 10.5 Ah Lithium-ion battery with 9.3 miles cruising range that can be recharged by an off-board battery charger, rated at input: 100-240 VAC / 50-60 Hz, and output: 24 VDC/ 2 A, that can be plugged into an AC outlet, when the device is not in use. Its overall dimensions are 33.6' x 22.6' x 35.8" (855 x 580 x 910 mm). The user can activate the joystick to move in the direction of the joystick is actuated. When the user releases the joystick, the device slows to stop and the brakes are automatically re-engaged. The maximum weight capacity of Joy Rider is 264 lbs. (120 kg) and its maximum speed is 3.75 mph (6 km/h).

I am sorry, but the provided text does not contain information about acceptance criteria and a study proving a medical AI device meets those criteria. The document is a 510(k) submission for an Electrically Powered Wheelchair, named Joy Rider, which is a physical medical device, not an AI-powered diagnostic or therapeutic tool.

The document discusses:

• A 510(k) premarket notification for a powered wheelchair.
• Indications for use, intended user population, and device description.
• Non-clinical performance tests conducted to comply with various ISO and ANSI/RESNA standards relevant to wheelchairs (e.g., static stability, dynamic stability, effectiveness of brakes, speed, obstacle climbing, electromagnetic compatibility).
• Biocompatibility tests for patient-contacting parts.
• A comparison of the subject device (Joy Rider) with a predicate device (Powered Wheelchair, DYW30A(D09)) to demonstrate substantial equivalence, discussing differences in components like motors, wheel sizes, maximum safe operational incline, dimensions, cruising range, and controllers, and asserting that these differences do not raise new safety or effectiveness concerns.

Therefore, I cannot provide the requested information about acceptance criteria and a study for an AI device.

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

The Be-Mobile 4-Wheeled Electric Scooter DK S280 is an indoor / outdoor electric scooter that is battery operated. It has a base with four-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.

This looks like a 510(k) premarket notification for a medical device, which is a regulatory submission to the FDA. It's not a study report designed to "prove" a device meets acceptance criteria in the way a clinical trial or performance study would. Rather, it aims to demonstrate substantial equivalence to a legally marketed predicate device.

Therefore, many of the requested elements about statistical sample sizes, expert adjudication, MRMC studies, and standalone performance metrics are not typically part of a 510(k) submission of this nature. The "performance testing" section refers to engineering standards met by the device, not a human reader or algorithm performance study.

Here's an attempt to answer as much as possible based on the provided text, acknowledging the limitations inherent in a 510(k) submission for an electric scooter:

1. Table of Acceptance Criteria and Reported Device Performance

For this specific 510(k) submission, the "acceptance criteria" are demonstrating compliance with relevant engineering standards and showing substantial equivalence to a predicate device. The performance isn't about diagnostic accuracy or clinical outcomes in the same way as a medical imaging AI, but rather about safety and functionality.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not applicable in the context of an "algorithm test set." The testing appears to be on a single device or a small sample to confirm engineering compliance.
• Data Provenance: The device manufacturer, TUNG KENG ENTERPRISE CO., LTD., is located in Taichung County, Taiwan. The specific engineering tests would have been performed by testing bodies or the manufacturer in compliance with the referenced standards. The data is prospective for the device being submitted.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Number of Experts: Not applicable. Ground truth in this context refers to the successful completion of engineering tests according to established standards. The "experts" would be the engineers and technicians performing and verifying compliance with the specified standards (e.g., ANSI / RESNA WC/Vol.2-1998, EMC standards), but they are not establishing a "ground truth" for a diagnostic outcome.
• Qualifications: As above, these would be qualified engineers and testers knowledgeable in the relevant standards.

4. Adjudication Method for the Test Set

• Not applicable. This is not a study requiring human adjudication for diagnostic outcomes. Compliance with engineering standards is typically determined by measurement against predefined thresholds.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, an MRMC comparative effectiveness study was not done. This type of study is relevant for diagnostic devices (e.g., AI for medical image analysis) to assess improvement in human reader performance with AI assistance. An electric scooter does not have human "readers" or "cases" in this sense.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• No, a standalone performance study as typically understood for an algorithm was not done. The device's "standalone" performance is assessed by its compliance with engineering standards (e.g., EMC, safety, and performance for electric wheelchairs/scooters) without a human operator performing a diagnostic task. The document details these compliance tests rather than algorithm-specific performance metrics.

7. The Type of Ground Truth Used

• The "ground truth" for this submission is compliance with established engineering and safety standards. This is demonstrated through measurement and testing against predefined criteria within the standards (e.g., electrical interference levels, stability, charger performance). It is not expert consensus, pathology, or outcomes data.

8. The Sample Size for the Training Set

• Not applicable. This device is an electric scooter, not an AI algorithm. Therefore, there is no "training set" in the context of machine learning.

9. How the Ground Truth for the Training Set Was Established

• Not applicable. As there is no training set for an AI algorithm, this question is not relevant.

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

The Be-Mobile 4-Wheeled Electric Scooter DK S600 is an indoor / outdoor electric scooter that is battery operated. It has a base with four-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 a medical device (Be-Mobile 4-Wheeled Electric Scooter, DK S600) seeking substantial equivalence to a legally marketed predicate device. This type of submission focuses on demonstrating that the new device is as safe and effective as an existing one, rather than presenting a performance study against predefined acceptance criteria for a novel medical device.

Therefore, many of the requested elements (like a table of acceptance criteria and reported performance, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth types) are not applicable or not detailed in this document.

However, I can extract information related to the performance testing that was conducted to establish substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated. The performance testing refers to standards (EMC Report ANSI / RESNA WC/Vol.2-1998, CISPR 11: 1990, EN61000-3-2: 1995, IEC61000-3-3: 1995). These are likely conducted on a small sample of the device itself (e.g., one or a few units) to verify compliance.
• Data Provenance: The testing was conducted by TUNG KENG Enterprise Co., Ltd. (Taiwan) and confirmed by UL certification in the context of a 510(k) submission. It represents prospective testing of the device for compliance with relevant standards.

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

• This information is not applicable to this type of regulatory submission. The "ground truth" here is compliance with established engineering and safety standards (e.g., EMC, RESNA). Certification bodies like UL (Underwriters Laboratories) are involved in verifying this compliance, but the number and specific qualifications of their individual experts are not detailed in this document.

4. Adjudication method for the test set

• Not applicable. Performance is assessed against quantitative standards, not through expert consensus on qualitative data.

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 device is an electric scooter, not an AI-enabled diagnostic or assistive system for human readers.

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

• Not applicable. This device is a mechanical and electronic mobility aid, not an algorithm.

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

• The "ground truth" used for this device's performance testing is adherence to international and national engineering and safety standards (e.g., ANSI / RESNA WC/Vol.2-1998, CISPR 11: 1990, EN61000-3-2: 1995, IEC61000-3-3: 1995) verified through testing and certification.

8. The sample size for the training set

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

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

• Not applicable.

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

The Be-Mobile 4-Wheeled Electric Scooter DK S500 is an indoor / outdoor electric scooter that is battery operated. It has a base with four-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 4-Wheeled Electric Scooter, DK S500. This type of submission focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets specific performance criteria through a detailed clinical study.

Therefore, the requested information regarding acceptance criteria, study details, sample sizes, expert involvement, and ground truth establishment is not present in the provided document.

Here's why and what information is available:

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

• Not Applicable. The document does not describe specific performance-based acceptance criteria for the scooter in terms of clinical outcomes or diagnostic accuracy. Instead, it relies on demonstrating that the new device meets established safety and performance standards and is substantially equivalent to a legally marketed predicate device.

• Reported Device Performance (from the document): The document states:

  • "The electronic systems between two devices are the same and all passed by the UL certificated, for instance the electronic controller, batteries and recharge. Thus the same safety level for the two devices is assured."
  • The device underwent "Performance Testing" to standards: ANSI / RESNA WC/Vol.2-1998, CISPR 11: 1990, EN61000-3-2: 1995, IEC61000-3-3: 1995 (Electrically powered wheelchairs, scooters, and their chargers - requirements and test methods).

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

• Not Applicable. No test set or clinical data is described in terms of a sample size. The "performance testing" referred to are likely engineering and safety tests on the device itself, not a study involving human subjects or clinical data in the way implied by the question.

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

• Not Applicable. As no clinical "test set" relying on a ground truth is described, there's no mention of experts or their qualifications in this context.

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

• Not Applicable. No adjudication method is described as there is no clinical test set requiring such a process.

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. An MRMC study is completely irrelevant to this device (an electric scooter). The device does not involve "readers" or "AI assistance."

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

• Not Applicable. This device is an electric scooter, it does not involve an algorithm or AI.

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

• Not Applicable. Ground truth in the context of clinical or diagnostic performance assessment is not relevant for this device. The "truth" here relates to compliance with engineering standards and safe operation, as demonstrated by the listed performance tests.

8. The sample size for the training set:

• Not Applicable. This device (an electric scooter) does not involve machine learning algorithms that would require a "training set."

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

• Not Applicable. See point 8.

Summary of what the document DOES tell us about the device's validation:

• Mechanism of Validation: The primary validation method for this 510(k) submission is demonstrating substantial equivalence to a previously legally marketed device (WU'S 4-WHEELED NEO SCOOTER WT-L4, K013763).
• Key Differences and Justification: The main differences identified are the type of electronic controller, overall dimensions, tire size, and weight. The submitter argues that the different controller (Dynamic Rhino DS72k vs. Curtis PMC) and physical appearance differences are not safety aspects and therefore do not negate substantial equivalence.
• Safety Assurance: The document emphasizes that the "electronic systems between two devices are the same and all passed by the UL certificated," ensuring the same safety level.
• Performance Standards: The device was tested against recognized industry standards for electrically powered wheelchairs, scooters, and their chargers (listed in section 1 above).

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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 S320 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 enboard battery charger.

Here's a breakdown of the acceptance criteria and study information for the Be-Mobile 3-wheeled Electric Scooter, DK S320, based on the provided FDA 510(k) summary:

This device is not an AI/ML powered device, therefore, some of the requested information (like multi-reader multi-case studies, standalone performance, training set details, etc.) are not applicable and will be marked as N/A.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a separate "test set" in the context of clinical trials or data for performance evaluation in the way AI/ML devices typically would. The performance testing refers to engineering standards applied to the device itself.

• Sample Size for Test Set: N/A (Performance testing was conducted on the device, not a distinct "test set" of patient data.)
• Data Provenance: N/A (The testing relates to product compliance with engineering standards, not patient data.)

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

N/A. Ground truth in this context refers to compliance with established engineering and safety standards, which are verified through defined test procedures. It doesn't involve expert medical interpretation of data.

4. Adjudication Method for the Test Set

N/A. Adjudication methods like 2+1 or 3+1 typically apply to human interpretation of medical images or data. For engineering standards, the outcome is pass/fail based on objective measurements against the standard's criteria.

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

N/A. This is not an AI/ML device.

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

N/A. This is not an AI/ML device.

7. The Type of Ground Truth Used

The ground truth used for this device's acceptance is compliance with established international and national engineering and safety standards (e.g., ANSI / RESNA WC/Vol.2-1998, CISPR 11, EN61000-3-2, IEC61000-3-3). The "performance testing" section indicates that the device "passed" these standards. Additionally, the basis for clearance is substantial equivalence to an existing legally marketed predicate device (WU`S 3-wheeled NEO SCOOTER WT-L3).

8. The Sample Size for the Training Set

N/A. This is not an AI/ML device; there is no "training set."

9. How the Ground Truth for the Training Set was Established

N/A. This is not an AI/ML device.

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