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The MobileCare™ Monitor 2100 system includes a MyPHD™ personal help device that is intended to monitor residents in home and long-term care facilities including independent living, assisted living and rehabilitation settings. The monitor can be placed on the wrist using the Velcro strap and used like a watch by the resident. The other form of MyPHD offered has no wrist straps so it can be clipped to the waist or used in a bandage for attachment at other locations on the person as may be appropriate or preferred by the user or healthcare provider. The system provides an alert to designated caregivers or professional staff automatically at pre-set thresholds to indicate an impact has occurred. The system also includes an emergency (panic) button that can be pressed by the monitored individual to alert caregivers as needed. The users of the system include staff and residents. The product is intended to be used on a 24-hour basis. The system is not intended to provide automated treatment decisions, nor is it to be used as a substitute for professional healthcare judgment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate healthcare professional.

The MobileCare™ Monitor 2100 system includes a MyPHD™ personal help device that is intended to monitor residents in home and long-term care facilities including independent living, assisted living and rehabilitation settings. The monitor can be placed on the wrist using the Velcro strap and used like a watch by the resident. The other form of MyPHD offered has no wrist straps so it can be clipped to the waist or used in a bandage for attachment at other locations on the person as may be appropriate or preferred by the user or healthcare provider. The system provides an alert to designated caregivers or professional staff automatically at pre-set thresholds to indicate an impact has occurred. The system also includes an emergency (panic) button that can be pressed by the monitored individual to alert caregivers as needed. The users of the system include staff and residents. The product is intended to be used on a 24-hour basis. The system is not intended to provide automated treatment decisions, nor is it to be used as a substitute for professional healthcare judgment. All patient medical diagnosis and treatment are to be performed under direct supervision and oversight of an appropriate healthcare professional.

The provided 510(k) summary for the KOGO 138 MobileCare Monitor™ does not contain information about specific acceptance criteria related to a clinical study or device performance metrics like sensitivity, specificity, or accuracy. It focuses on demonstrating substantial equivalence to predicate devices primarily through comparison of technical characteristics and intended use, and conformance to non-clinical safety (FCC regulations) and software validation standards.

Therefore, the following information cannot be extracted from the provided text:

• A table of acceptance criteria and the reported device performance: No specific performance metrics or acceptance criteria are stated. The document indicates software validation was performed, but no results are provided.
• Sample size used for the test set and the data provenance: Not applicable as no clinical test set is described.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
• Adjudication method for the test set: Not applicable.
• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The device is a monitor with alerting capabilities; performance is not typically described in terms of "algorithm only" in this context. The document mentions an "impact sensor that may indicate a fall" and an "emergency (panic) button."
• The type of ground truth used: Not applicable as no clinical performance study is detailed with ground truth.
• The sample size for the training set: Not applicable as a machine learning training set is not mentioned for this device type.
• How the ground truth for the training set was established: Not applicable.

Here's a summary of the available information regarding acceptance criteria and studies:

1. Acceptance Criteria and Reported Device Performance:

The document primarily relies on demonstrating substantial equivalence to predicate devices and adherence to non-clinical standards rather than clinical performance metrics.

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

• Sample Size: Not specified, as no clinical test set for performance evaluation is described in the provided text. The evaluation focuses on non-clinical aspects and substantial equivalence.
• Data Provenance: Not applicable, as no clinical performance data are presented.

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 requiring expert-established ground truth is described.

4. Adjudication method for the test set:

• Not applicable, as no clinical test set requiring adjudication is described.

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, a multi-reader multi-case comparative effectiveness study was not done or reported. This type of study is more common for diagnostic imaging AI devices, whereas the MobileCare Monitor™ is a monitoring and alerting system.

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

• The document does not describe a "standalone" algorithmic performance study in the context typically used for AI/ML devices (e.g., measuring accuracy of an image analysis algorithm). The device itself functions as a standalone monitoring system that provides alerts.

7. The type of ground truth used:

• Not applicable, as no clinical performance study with defined ground truth is described. The "performance" aspect refers to software validation.

8. The sample size for the training set:

• Not applicable, as no machine learning algorithm requiring a training set is explicitly mentioned or detailed in the provided information. The device functions based on sensors and pre-set thresholds, not a learned model.

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

• Not applicable, as no machine learning algorithm or training set is described.

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