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The Proteus Patch is a miniaturized, wearable data-logger for ambulatory recording of physiological and behavioral metrics such as heart rate, activity, body angle relative to gravity, and time-stamped, patient-logged events, including events signaled by swallowing the Ingestible Sensor accessory. The Proteus Patch enables unattended data collection for clinical and research applications. The Proteus Patch may be used in any instance where quantifiable analysis of event-associated physiological and behavioral metrics is desirable.

The Proteus Patch is a body-worn sensor that collects physiological and behavioral metrics such as heart tate, activity, body angle relative to gravity, and time-stamped user-logged events generated by swallowing the Proteus Ingestible Sensor. The Ingestible Sensor is embedded inside an inactive tablet (the Pill) for ease of handling and swallowing. Once the Ingestible Sensor reaches the stomach, it activates and communicates its presence and unique identifier to the Patch stores and wirelessly sends the physiological, behavioral, event, and ingestion data to a general computing device for display.

Here's an analysis of the acceptance criteria and study information provided in the document:

1. Table of Acceptance Criteria and Reported Device Performance

The provided summary does not explicitly state formal "acceptance criteria" with specific thresholds for performance. Instead, it describes general methods for validation and testing. Therefore, the table below interprets the "acceptance criteria" as the method of validation itself rather than a numerical threshold, and the "reported device performance" as the statement of successful validation.

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

The document does not provide specific sample sizes for test sets used for the individual performance validations. It mentions testing methods but not the number of subjects or items tested.
The data provenance is not specified (e.g., country of origin, retrospective/prospective). This is a non-clinical evaluation, so "patients" might not have been involved in all tests.

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

This document describes technical device validation against standards or known inputs (e.g., "known acceleration"). It does not involve human expert interpretation of data to establish ground truth in the way a diagnostic AI system would. Therefore, this information is not applicable or provided.

4. Adjudication Method for the Test Set

Not applicable, as this is a technical validation against established standards and known physical inputs, not a subjective interpretation requiring expert adjudication.

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

No, an MRMC comparative effectiveness study was not done. The document states: "No additional clinical data were required to confirm substantial equivalence to predicate device." This indicates the device was cleared based on non-clinical performance and technological characteristics in comparison to a predicate, not through a study involving human readers or AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The performance data described (heart rate algorithm, accelerometer validation, ingestion sensor testing) are inherently "standalone" in the sense that they are assessing the device's technical capabilities without a human-in-the-loop for the primary validation. The device's overall intended use does involve data collection for clinical and research applications, implying potential future human interpretation, but the core performance described here is the device's ability to accurately log these metrics.

7. The Type of Ground Truth Used

The ground truth used for validation was primarily:

• Established industry standards: For heart rate, "guidelines set forth in the ANSI/AAMI EC 13 standard."
• Known physical inputs: For activity/body angle, "a known acceleration applied against each of its three axes."
• Device-specific measurements/specifications: For the ingestion sensor, "activation time and lifetime after activation" would be measured against the sensor's designed parameters.

8. The Sample Size for the Training Set

The document does not mention any "training set" in the context of machine learning. The device utilizes algorithms (e.g., modified Hamilton-Tompkins for R-wave frequency) that would have been developed and potentially trained previously, but the details of such training (including sample size) are not provided or relevant to this 510(k) summary, which focuses on validation rather than algorithm development.

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

As no training set is discussed, this information is not provided.

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