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The CNMPs primary intended use is for muscular injury testing. It strives to achieve this goal through non-invasive testing, using range-of-motion and functional capacity integration, as well as using standard EMG sensors. The CNMP is specifically designed to testing the cervical, thoracic and the upper and lower extremities. It observes muscle functioning and characteristics. These functions and characteristics include muscle tone, fatigue, and a number of activities which take place in the muscle. The CNMP can be used in a number of arenas, such as sports medicine, rehabilitation clinics, employee evaluation, and litigation.
Surface electromyography with range of motion tracking, functional capacity and grip strength measuring.

The Comprehensive Neuromuscular Profiler is a muscle and range of motion monitoring system that is portable, stand-alone and in a convenient package suitable for use in a medical environment. The system consists of a number of sensors to be connected to various parts of the human body which will be fed into a customized enclosure containing all connectors and mediums for conditioning, acquiring and transferring the sensor data. The conditioned data will then be sent to the notebook computer (PC) that is connected to the enclosure. The system will be capable of monitoring and recording data from electromyography (EMG) sensors connected to various muscle groups in the human body. During the acquisition of EMG signals, the system will simultaneously acquire motion of the body and/or muscle strength.

This product is a medical device, and its 510(k) summary does not contain the detailed information about acceptance criteria and study results typically found in publications for diagnostic algorithms. The document focuses on establishing substantial equivalence to predicate devices rather than providing a performance study report with specific metrics.

Therefore, many of the requested details cannot be extracted from the provided text.

Here is an attempt to answer the questions based only on the provided text, with clear indications where information is missing:

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

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

• Sample Size (Test Set): Not mentioned.
• Data Provenance: Not mentioned. The document describes the device and its intended use but does not detail any specific clinical studies or datasets used for testing.

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)

• Not mentioned. The document does not describe any specific ground truth establishment process or involvement of experts for a test set.

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

• Not mentioned. No adjudication method 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

• A MRMC comparative effectiveness study is not mentioned and is highly unlikely given the nature of the device (a diagnostic electromyograph for data acquisition, not an AI-powered image analysis tool for human readers). The device itself is for monitoring and recording data, not assisting human readers with interpretation in the way AI would for, say, radiology.

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

• This is not applicable in the typical sense of "standalone algorithm performance" for an AI diagnostic device. The Comprehensive Neuromuscular Profiler (CNMP) is a physical monitoring system that acquires data from sensors. Its performance would be related to the accuracy and reliability of its data acquisition, not an algorithm's diagnostic output. The document states it is a "stand-alone" system in terms of portability and packaging, but this refers to its physical characteristics, not its analytical function as an AI algorithm.

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

• Not mentioned. The document does not describe any specific ground truth used for evaluating the device's performance.

8. The sample size for the training set

• Not applicable/Not mentioned. This device is a diagnostic electromyograph system for data acquisition, not an AI/ML model that requires a training set.

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

• Not applicable. As this is not an AI/ML model, there is no training set mentioned or a ground truth establishment process for it.

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