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The PAM-RL is a small, limb-worn activity monitor, typically placed on the leg or wrist, designed for documenting physical movements associated with applications in physiological monitoring. The device is intended to monitor limb activity associated with movement during sleep. The unit can also be used to assess activity in any instance where quantifiable analysis of physical motion is desirable.

The PAM-RL is a compact, limb-worn, battery-operated activity monitor. The monitor consists of the activity monitor itself and a velcro band. The PAM-RL is intended for the measurement, storage, and analysis of limb activity. The PAM-RL can be attached to the subject's limb and through the use of an accelerometer, motion of that limb is measured, the activity is stored within the activity monitor. A computer program is used to set up the PAM-RL to collect data. This program runs on an IBM-compatible personal computer (PC). The major functions of the application software are to program the device to collect data, retrieve the data from the activity monitor, display the data, and to store the data for future reference and comparison. The PAM-RL uses a smart download cable to provide a communiations link between the PAM-RL and the PC. To download data from the PAM-RL to the PC, one end of the PAM-RL's smart cable is inserted into the PAM-RL's port via a miniature 2.5 mm phone plug and the other end connected to the serial communications port of the PC via a standard 9-pin RS-232 COM port. The PAM-RL utilizes a motion sensor known as an accelerometer to monitor the occurrence and degree of motion. This type of sensor provides an analog signal where the amplitude and speed of motion produces a signal whose magnitude and duration depend on the amount of motion. The activity signals are amplified and digitized by the on-board circuit. This information is stored in memory on board the device as activity counts. Activity can alternatively be stored in units of milli-g.

This 510(k) premarket notification for the PAM-RL device does notContain a detailed study section with specific acceptance criteria or a comprehensive study design that quantifies device performance against those criteria. The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with detailed statistical analysis.

Therefore, many of the requested details cannot be extracted from this document. However, based on the information provided, here's what can be gathered:

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

This information is not explicitly provided in the 510(k) summary. Acceptance criteria for device performance were not presented, nor were specific quantified performance metrics for the PAM-RL device in a comparative study against such criteria. The submission aims to establish substantial equivalence to a predicate device, ACTITRAC (K992410), implying that its performance is considered acceptable if analogous to the predicate.

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

This information is not provided in the 510(k) summary. The document does not describe any specific clinical or performance test set, its sample size, or data provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience):

This information is not provided. Since no specific test set or performance study is detailed, there's no mention of experts establishing ground truth.

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

This information is not provided.

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:

This information is not provided. The device is an activity monitor, not an AI-assisted diagnostic tool, and the summary does not describe any MRMC studies or human-in-the-loop performance evaluations.

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

The 510(k) summary describes the device's functionality and its basic scientific concepts. It states that the PAM-RL "utilizes a motion sensor known as an accelerometer to monitor the occurrence and degree of motion" and that "activity is stored within the activity monitor." A "computer program is used to set up the PAM-RL to collect data," "retrieve the data from the activity monitor, display the data, and to store the data for future reference and comparison."

This implies that the device functions to collect and record data automatically (standalone). However, the document does not contain a specific "standalone performance study" section with metrics and results. Its design suggests it operates independently to collect motion data without real-time human intervention in the data acquisition phase.

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

This information is not provided. Without a described performance study, the type of ground truth used is not mentioned.

8. The sample size for the training set:

This information is not provided. As this is not an AI/machine learning device in the contemporary sense, there's no "training set" mentioned for an algorithm. The device measures physical motion via an accelerometer.

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

This information is not provided.

Summary of available information regarding performance:

The document focuses on the description and function of the PAM-RL, emphasizing its ability to measure, store, and analyze limb activity via an accelerometer. The primary "proof" of its acceptability for market clearance appears to be its substantial equivalence to a legally marketed predicate device, "ACTITRAC" (K992410). The implication is that if the new device functions similarly to an already approved device for the same intended use, its performance is considered acceptable for market. No specific performance metrics or studies are detailed in this 510(k) summary.

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The ActiTrac is a small wrist-worn activity monitor designed for documenting physical movements associated with applications in physiological monitoring. The device is intended to be used to analyze circadian rhythms, automatically collect and score data for sleep parameters. These parameters, representing the number and intensity of limb movements, are directly correlated to sleep efficiency. The unit can also be used to assess activity in any instance where quantifiable analysis of physical motion is desirable.

The ActiTrac is a compact, wrist-worn, battery-operated activity monitor whose physical characteristics are similar to a small wristwatch. The monitor consists of the activity monitor itself and a wrist band. The ActiTrac is intended for the measurement, storage, and analysis of body activity. The ActiTrac can be attached to the subject's limb and through the use of an accelerometer, motion of that limb is measured, the activity stored within the activity monitor. A computer program is used to set up the ActiTrac to collect data. This program runs on an IBM-compatible personal computer (PC). The major functions of the application software are to program the device to collect data, retrieve the data from the activity monitor, display the data, and to store the data for future reference and comparison. The ActiTrac uses a smart download cable to provide a communications link between the ActiTrac and the PC. The ActiTrac utilizes a motion sensor known as an "accelerometer" to monitor the occurrence and degree of motion. This type of sensor provides an analog signal where the amplitude and speed of motion produces a signal whose magnitude and duration depend on the amount of motion. The activity signals are amplified and digitized by the on-board circuit. This information is stored in memory on board the device as activity counts. Activity can alternatively be stored in units of milli-g.

Here's an analysis of the provided text, focusing on acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation of Implicit Criteria:

The document doesn't explicitly state numerical "acceptance criteria" in the way a modern regulatory filing might. Instead, the performance is assessed primarily through substantial equivalence to a predicate device (ActiWatch) and through a specific calibration process. The acceptance criteria are largely implied by meeting or exceeding the performance characteristics of the predicate, and by demonstrating reliable measurement compared to a "gold standard."

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

• Sample Size for Test Set: Not explicitly mentioned for human subjects. For the technical calibration, it refers to "the device" (singular) being subjected to motion, implying testing of individual units.
• Data Provenance: The study described for "Counts vs. Motion" and "Variation between devices" appears to be prospective laboratory testing. No information is provided regarding the country of origin of the data as it's a technical performance test, not a clinical trial involving human subjects from a specific country.

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

• Experts: Not applicable. The "ground truth" for the technical performance tests described (sensitivity and accuracy) was established using a gold standard accelerometer and a controlled moving platform, not human experts.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. The "ground truth" was established objectively through instrumentation (gold standard accelerometer) and mechanical means (moving platform), not through human expert adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study: No. The provided text does not describe an MRMC comparative effectiveness study involving human readers. The device is a physical activity monitor, and its performance evaluation focuses on the accuracy and consistency of its physical measurements, not on human interpretation of data.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study: Yes. The described performance assessment ("Counts vs. Motion" and "Variation between devices") is a standalone assessment of the device's ability to accurately measure and quantify motion. The device's output (activity counts) is directly compared to a known physical input (uniform harmonic motion and gold standard accelerometer data). There is no "human-in-the-loop" during this specific performance evaluation.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth used for the performance evaluation is instrumentation-based and defined physical parameters. Specifically:
  • For "Counts vs. Motion": A "gold standard accelerometer also placed on the moving platform" and the precisely controlled parameters of the moving platform (constant frequency of 3.968 Hz and an amplitude of 0.2 g).
  • For "Variation between devices": An "established laboratory standard" used for calibration.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. The ActiTrac is a hardware device with an accelerometer and internal processing logic; it does not appear to use a machine learning algorithm that requires a "training set" in the conventional sense. Its "training" or calibration is based on physical measurement against established laboratory standards.

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

• Ground Truth for Training Set: Not applicable, as no machine learning training set is mentioned. The device's calibration and "training" (if loosely interpreted as initial setup or manufacturing verification) involve comparing manufactured units to "established laboratory standards" and calibrating them to meet output specifications, as described in "Variation between devices." This is a quality control/calibration process, not a machine learning training process.

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