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The Vital Sign Monitoring Sensor (Model XK300) is intended to measure heart rate and respiration rate in adult patients in a general care hospital environment including nursing homes. The Vital Sign Monitoring Sensor can be used for home healthcare for data collection to inform patient care but not to acutely treat a patient. XK300 monitors presence or absence of a patient in detection area of within 7 meters. The XK30 also monitors the length of continuous patient motion or absence of patient motion.

The Vital Sign Monitoring Sensor (Model XK300) measures heart rate, respiratory rate (breathing rate), and movement of people with very little or no movement (rest mode) using Impulse Radio Ultra-Wideband (IR UWB) radar technology. The heart rate and respiratory rate are measured by detecting minute displacements of the chest and converting the movement into the number of breaths and heart beats per minute.

The provided document is a 510(k) summary for the Xandar Kardian Vital Sign Monitoring Sensor (Model XK300). It focuses on establishing substantial equivalence to a predicate device rather than presenting a detailed study proving the device meets specific acceptance criteria with quantifiable metrics. Therefore, some information requested in the prompt, such as specific acceptance criteria for performance, detailed study designs, sample sizes for test sets, expert qualifications, and ground truth establishment methods, are not explicitly provided in the text.

However, based on the available information, here's what can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states that "performance testing has confirmed that this difference does not affect the performance of the Vital Signs Monitoring Sensor." This implies that the device's performance, despite having different measurement ranges than the predicate, was deemed acceptable. However, specific, quantifiable acceptance criteria (e.g., accuracy, precision, bias) for heart rate and respiration rate, and the corresponding reported performance values from a dedicated study, are not provided. The comparison table (Table 5-1) mainly focuses on technical specifications and intended use comparison with the predicate, not performance metrics against acceptance criteria.

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

The document mentions "performance testing" was conducted, but it does not specify the sample size used for this testing. It also does not mention the data provenance (e.g., country of origin, retrospective or prospective nature).

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

The document does not provide information regarding the number of experts used or their qualifications for establishing ground truth in performance testing.

4. Adjudication Method for the Test Set:

The document does not specify an adjudication method for the test set.

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

The document does not mention an MRMC comparative effectiveness study involving human readers.

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

The document implies standalone performance testing was done, as it discusses "performance testing" of the device itself and its ability to measure heart rate and respiration rate. The description of the device as a "sensor" that detects minute displacements and converts them into rates suggests it operates autonomously. However, it does not explicitly label a study as a "standalone" study with detailed methodology.

7. The Type of Ground Truth Used:

The document implying heart rate and respiration rate measurement suggests the ground truth for performance testing would likely be established using reference medical devices (e.g., ECG for heart rate, capnography or spirometry for respiration rate) that are considered gold standards for these physiological measurements. However, the document does not explicitly state the type of ground truth used.

8. The Sample Size for the Training Set:

The document does not mention a training set or its sample size. This is common in a 510(k) submission where the focus is on substantial equivalence to a predicate rather than a de novo approval requiring extensive AI model validation. The device description suggests a sensor-based measurement system rather than a deep learning algorithm that typically requires a large training set.

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

Since a training set is not mentioned, the method for establishing its ground truth is also not provided.

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